Today, the number of researches based on the data they move known as mobile objects indexing came out from the traditional static one. There are some indexing approaches to handle the complicated moving positions. One of the suitable ideas is pre-ordering these objects before building index structure. In this paper, a structure, a presorted-nearest index tree algorithm is proposed that allowed maintaining, updating, and range querying mobile objects within the desired period. Besides, it gives the advantage of an index structure to easy data access and fast query along with the retrieving nearest locations from a location point in the index structure. A synthetic mobile position dataset is also proposed for performance evaluation so that it is free from location privacy and confidentiality. The detail experimental results are discussed together with the performance evaluation of KDtree-based index structure. Both approaches are similarly efficient in range searching. However, the proposed approach is especially much more save time for the nearest neighbor search within a range than KD tree-based calculation.
A Study of Mobile User Movements Prediction Methods IJECEIAES
For a decade and more, the Number of smart phone users count increasing day by day. With the drastic improvements in Communication technologies, the prediction of future movements of mobile users needs also have important role. Various sectors can gain from this prediction. Communication management, City Development planning, and locationbased services are some of the fields that can be made more valuable with movement prediction. In this paper, we propose a study of several Location Prediction Techniques in the following areas.
This document describes a proposed multi-agent system for searching distributed data. The system uses three types of agents - coordinator agents, search agents, and local agents. Coordinator agents coordinate the retrieval process by creating search agents and collecting results. Search agents carry queries to nodes containing relevant databases. Local agents reside at nodes with databases, accept queries from search agents, search the databases for answers, and return results to the search agents. The system aims to retrieve data from distributed databases with minimum network bandwidth consumption using this multi-agent approach.
Internet becomes the most popular surfing environment which increases the
service oriented data size. As the data size grows, finding and retrieving the most
similar data from the large volume of data would become more difficult task. This
problem is focused in the various research methods, which attempts to cluster the
large volume of data. In the existing research method Clustering-based Collaborative
Filtering approach (ClubCF) is introduced whose main goal is to cluster the similar
kind of data together, so that retrieval time cost can be reduced considerably.
However, existing research methods cannot find the similar reviews accurately which
needs to be focused more for efficient and accurate recommendation system. This is
ensured in the proposed research method by introducing the novel research technique
namely Modified Collaborative Filtering and Clustering with Regression (MoCFCR).
In this research method, initially k means algorithm is used to cluster the similar
movie reviewer together, so that recommendation process can be done in the easier
way. In order to handle the large volume of data this research work adapts the map
reduce framework which will divide the entire data into subsets which will assigned
on separate nodes with individual key values. After clustering, the clustered outcome
is merged together using inverted index procedure in which similarity between movies
would be calculated. Here collaborative filtering is applied to remove the movies that
are not relevant to input. Finally recommendations of movies are made in the accurate
way by using the logistic regression method. The overall evaluation of the proposed
research method is done in Hadoop from which it can be proved that the proposed
research technique can lead to provide better outcome than the existing research
techniques
Multi Similarity Measure based Result Merging Strategies in Meta Search EngineIDES Editor
In Meta Search Engine result merging is the key
component. Meta Search Engines provide a uniform query
interface for Internet users to search for information.
Depending on users’ needs, they select relevant sources and
map user queries into the target search engines, subsequently
merging the results. The effectiveness of a Meta Search
Engine is closely related to the result merging algorithm it
employs. In this paper, we have proposed a Meta Search
Engine, which has two distinct steps (1) searching through
surface and deep search engine, and (2) Ranking the results
through the designed ranking algorithm. Initially, the query
given by the user is inputted to the deep and surface search
engine. The proposed method used two distinct algorithms
for ranking the search results, concept similarity based
method and cosine similarity based method. Once the results
from various search engines are ranked, the proposed Meta
Search Engine merges them into a single ranked list. Finally,
the experimentation will be done to prove the efficiency of
the proposed visible and invisible web-based Meta Search
Engine in merging the relevant pages. TSAP is used as the
evaluation criteria and the algorithms are evaluated based on
these criteria.
A Web Extraction Using Soft Algorithm for Trinity Structureiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Time-Ordered Collaborative Filtering for News RecommendationIRJET Journal
This document presents a novel news recommendation system called Time-Ordered Collaborative Filtering for News Recommendation. The system filters and recommends news to users based on their choices and preferences using collaborative filtering and content-based filtering. It extracts news data from popular online newspapers and stores it in a database. The recommendation framework then preprocesses the news data and applies algorithms like calculating sparse matrices to rank news and recommend the top news to each user based on their interests.
Study on Theoretical Aspects of Virtual Data Integration and its ApplicationsIJERA Editor
Data integration is the technique of merging data residing at different sources at different locations, and
providing users with an integrated, reconciled view of these data. Such unified view is called global or mediated
schema. It represents the intentional level of the integrated and reconciled data. In the data integration system,
our area of interest in this paper is characterized by an architecture based on a global schema and a set of
sources or source schemas. The objective of this paper is to provide a study on the theoretical aspects of data
integration systems and to present a comprehensive review of the applications of data integration in various
fields including biomedicine, environment, and social networks. It also discusses a privacy framework for
protecting user’s privacy with privacy views and privacy policies.
The huge volume of text documents available on the internet has made it difficult to find valuable
information for specific users. In fact, the need for efficient applications to extract interested knowledge
from textual documents is vitally important. This paper addresses the problem of responding to user
queries by fetching the most relevant documents from a clustered set of documents. For this purpose, a
cluster-based information retrieval framework was proposed in this paper, in order to design and develop
a system for analysing and extracting useful patterns from text documents. In this approach, a pre-
processing step is first performed to find frequent and high-utility patterns in the data set. Then a Vector
Space Model (VSM) is performed to represent the dataset. The system was implemented through two main
phases. In phase 1, the clustering analysis process is designed and implemented to group documents into
several clusters, while in phase 2, an information retrieval process was implemented to rank clusters
according to the user queries in order to retrieve the relevant documents from specific clusters deemed
relevant to the query. Then the results are evaluated according to evaluation criteria. Recall and Precision
(P@5, P@10) of the retrieved results. P@5 was 0.660 and P@10 was 0.655.
A Study of Mobile User Movements Prediction Methods IJECEIAES
For a decade and more, the Number of smart phone users count increasing day by day. With the drastic improvements in Communication technologies, the prediction of future movements of mobile users needs also have important role. Various sectors can gain from this prediction. Communication management, City Development planning, and locationbased services are some of the fields that can be made more valuable with movement prediction. In this paper, we propose a study of several Location Prediction Techniques in the following areas.
This document describes a proposed multi-agent system for searching distributed data. The system uses three types of agents - coordinator agents, search agents, and local agents. Coordinator agents coordinate the retrieval process by creating search agents and collecting results. Search agents carry queries to nodes containing relevant databases. Local agents reside at nodes with databases, accept queries from search agents, search the databases for answers, and return results to the search agents. The system aims to retrieve data from distributed databases with minimum network bandwidth consumption using this multi-agent approach.
Internet becomes the most popular surfing environment which increases the
service oriented data size. As the data size grows, finding and retrieving the most
similar data from the large volume of data would become more difficult task. This
problem is focused in the various research methods, which attempts to cluster the
large volume of data. In the existing research method Clustering-based Collaborative
Filtering approach (ClubCF) is introduced whose main goal is to cluster the similar
kind of data together, so that retrieval time cost can be reduced considerably.
However, existing research methods cannot find the similar reviews accurately which
needs to be focused more for efficient and accurate recommendation system. This is
ensured in the proposed research method by introducing the novel research technique
namely Modified Collaborative Filtering and Clustering with Regression (MoCFCR).
In this research method, initially k means algorithm is used to cluster the similar
movie reviewer together, so that recommendation process can be done in the easier
way. In order to handle the large volume of data this research work adapts the map
reduce framework which will divide the entire data into subsets which will assigned
on separate nodes with individual key values. After clustering, the clustered outcome
is merged together using inverted index procedure in which similarity between movies
would be calculated. Here collaborative filtering is applied to remove the movies that
are not relevant to input. Finally recommendations of movies are made in the accurate
way by using the logistic regression method. The overall evaluation of the proposed
research method is done in Hadoop from which it can be proved that the proposed
research technique can lead to provide better outcome than the existing research
techniques
Multi Similarity Measure based Result Merging Strategies in Meta Search EngineIDES Editor
In Meta Search Engine result merging is the key
component. Meta Search Engines provide a uniform query
interface for Internet users to search for information.
Depending on users’ needs, they select relevant sources and
map user queries into the target search engines, subsequently
merging the results. The effectiveness of a Meta Search
Engine is closely related to the result merging algorithm it
employs. In this paper, we have proposed a Meta Search
Engine, which has two distinct steps (1) searching through
surface and deep search engine, and (2) Ranking the results
through the designed ranking algorithm. Initially, the query
given by the user is inputted to the deep and surface search
engine. The proposed method used two distinct algorithms
for ranking the search results, concept similarity based
method and cosine similarity based method. Once the results
from various search engines are ranked, the proposed Meta
Search Engine merges them into a single ranked list. Finally,
the experimentation will be done to prove the efficiency of
the proposed visible and invisible web-based Meta Search
Engine in merging the relevant pages. TSAP is used as the
evaluation criteria and the algorithms are evaluated based on
these criteria.
A Web Extraction Using Soft Algorithm for Trinity Structureiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Time-Ordered Collaborative Filtering for News RecommendationIRJET Journal
This document presents a novel news recommendation system called Time-Ordered Collaborative Filtering for News Recommendation. The system filters and recommends news to users based on their choices and preferences using collaborative filtering and content-based filtering. It extracts news data from popular online newspapers and stores it in a database. The recommendation framework then preprocesses the news data and applies algorithms like calculating sparse matrices to rank news and recommend the top news to each user based on their interests.
Study on Theoretical Aspects of Virtual Data Integration and its ApplicationsIJERA Editor
Data integration is the technique of merging data residing at different sources at different locations, and
providing users with an integrated, reconciled view of these data. Such unified view is called global or mediated
schema. It represents the intentional level of the integrated and reconciled data. In the data integration system,
our area of interest in this paper is characterized by an architecture based on a global schema and a set of
sources or source schemas. The objective of this paper is to provide a study on the theoretical aspects of data
integration systems and to present a comprehensive review of the applications of data integration in various
fields including biomedicine, environment, and social networks. It also discusses a privacy framework for
protecting user’s privacy with privacy views and privacy policies.
The huge volume of text documents available on the internet has made it difficult to find valuable
information for specific users. In fact, the need for efficient applications to extract interested knowledge
from textual documents is vitally important. This paper addresses the problem of responding to user
queries by fetching the most relevant documents from a clustered set of documents. For this purpose, a
cluster-based information retrieval framework was proposed in this paper, in order to design and develop
a system for analysing and extracting useful patterns from text documents. In this approach, a pre-
processing step is first performed to find frequent and high-utility patterns in the data set. Then a Vector
Space Model (VSM) is performed to represent the dataset. The system was implemented through two main
phases. In phase 1, the clustering analysis process is designed and implemented to group documents into
several clusters, while in phase 2, an information retrieval process was implemented to rank clusters
according to the user queries in order to retrieve the relevant documents from specific clusters deemed
relevant to the query. Then the results are evaluated according to evaluation criteria. Recall and Precision
(P@5, P@10) of the retrieved results. P@5 was 0.660 and P@10 was 0.655.
Matching GPS Traces with Personal
Schedules,” Proc. First ACM Int’l Workshop
Personalized Context Modeling and
Management for UbiComp Applications
(PCM), 2009.
[8] X. Li, Y.-Y. Chen, T. Suel, and A.
Markowetz, “Efficient Query Processing in
Geographic Web Search,” Proc. Int’l ACM
SIGIR Conf. Research and Development in
Information Retrieval (SIGIR), 2006.
[9] B.J. Jansen, A. Spink, and T. Saracevic,
“Real Life, Real Users, and Real Needs: A
Study and Analysis of User Queries
IRJET- Cluster Analysis for Effective Information Retrieval through Cohesive ...IRJET Journal
This document discusses using document clustering to improve information retrieval systems. It proposes a framework with four steps: 1) the information retrieval system retrieves documents based on a user query, 2) a similarity measure is used to determine document similarity, 3) the documents are clustered based on similarity, and 4) the clusters are ranked based on relevance to the query. The goal of clustering is to group relevant documents together to help users more easily find needed information. Different clustering algorithms are reviewed, noting that hierarchical clustering and overlapping clusters may improve search results over other methods.
This document summarizes various techniques for scalable continual top-k keyword search in relational databases. There are two main approaches: schema-based and graph-based. Schema-based methods generate candidate networks from the database schema and evaluate them. Graph-based methods represent the database as a graph and use techniques like bidirectional expansion. Top-k keyword search finds the highest scoring k results instead of all results. Methods like the Global Pipeline algorithm and Skyline-Sweeping algorithm efficiently process top-k queries over multiple candidate networks. Techniques for updating results with database changes include maintaining an initial top-k and recalculating scores. Lattice-based methods share computational costs for keyword search in data streams.
Survey on scalable continual top k keyword search in relational databaseseSAT Journals
Abstract Keyword search in relational database is a technique that has higher relevance in the present world. Extracting data from a large number of sets of database is very important .Because it reduces the usage of man power and time consumption. Data extraction from a large database using the relevant keyword based on the information needed is a very interactive and user friendly. Without knowing any database schemas or query languages like sql the user can get information. By using keyword in relational database data extraction will be simpler. The user doesn’t want to know the query language for search. But the database content is always changing for real time application for example database which store the data of publication data. When new publications arrive it should be added to database so the database content changes according to time. Because the database is updated frequently the result should change. In order to handle the database updation takes the top-k result from the currently updated data for each search. Top-k keyword search means take greatest k results based on the relevance of document. Keyword search in relational database means to find structural information from tuples from the database. Two types of keyword search are schema-based method and graph based approach. Using top-k keyword search instead of executing all query results taking highest k queries. By handling database updation try to find the new results and remove expired one
This document discusses domain specific search and ranking model adaptation using a binary classifier. It proposes using a ranking adaptation support vector machine (RA-SVM) to adapt an existing broad-based ranking model to a new targeted domain with limited labeled data. The RA-SVM utilizes predictions from the existing model as well as domain specific features to build a robust ranking model for the target domain. It evaluates the adapted model using precision, recall, average precision and Kendall's Tau metrics. The goal is to minimize the amount of labeled data needed for the target domain while still achieving good performance.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IRJET- Missing Data Imputation by Evidence ChainIRJET Journal
This document presents a new method called "evidence chain" for imputing missing data. The method works as follows:
1. It first identifies missing data values marked as "-1" in a dataset.
2. It then combines attribute values associated with each missing data point to form "evidence chains" to estimate potential values.
3. It calculates possible values and their probabilities for each missing data point.
4. It checks if an evidence chain matches values in the data and uses the most probable or highest value to impute the missing data.
5. The imputed values replace the original missing values to complete the dataset. The method is implemented in an application that can generate test data with missing values.
An effective search on web log from most popular downloaded contentijdpsjournal
A Web page recommender system effectively predicts the best related web page to search. While search
ing
a word from search engine it may display some unnecessary links and unrelated data’s to user so to a
void
this problem, the con
ceptual prediction model combines both the web usage and domain knowledge. The
proposed conceptual prediction model automatically generates a semantic network of the semantic Web
usage knowledge, which is the integration of domain knowledge and web usage i
nformation. Web usage
mining aims to discover interesting and frequent user access patterns from web browsing data. The
discovered knowledge can then be used for many practical web applications such as web
recommendations, adaptive web sites, and personali
zed web search and surfing
The document describes a proposed patent search system that aims to improve the usability of patent searches. It discusses modules for login, query processing, error correction, query suggestion, ranking results, and partitioning patents. The goal is to make the search process easier for users by correcting errors, expanding queries, and efficiently retrieving the most relevant results. Key techniques include topic modeling for suggestions, error correction using tries, and partitioning patents into groups for faster searching.
IRJET- Semantics based Document ClusteringIRJET Journal
This document describes a proposed ontology-based document clustering system. The system uses a two-step clustering algorithm that first applies K-means partitioning clustering followed by hierarchical agglomerative clustering. Ontology is introduced through a weighting scheme that integrates traditional TF-IDF word weights with weights of semantic relations between words from the ontology. The goal is to produce document clusters that are semantically meaningful by accounting for relationships between words, rather than just word co-occurrence. An overview of the system architecture and modules is provided, along with descriptions of preprocessing, concept weighting, clustering approaches, and initial implementation results.
Correlation of artificial neural network classification and nfrs attribute fi...eSAT Journals
Abstract
Mostly 5 to 15% of the women in the stage of reproduction face the disease called Polycystic Ovarian Syndrome (PCOS) which is the multifaceted, heterogeneous and complex. The long term consequences diseases like endometrial hyperplasia, type 2 diabetes mellitus and coronary disease are caused by the polycystic ovaries, chronic anovulation and hyperandrogenism are characterized with the resistance of insulin and the hypertension, abdominal obesity and dyslipidemia and hyperinsulinemia are called as Metabolic syndrome (frequent metabolic traits) The above cause the common disease called Anovulatory infertility. Computer based information along with advanced Data mining techniques are used for appropriate results. Classification is a classic data mining task, with roots in machine learning. Naïve Bayesian, Artificial Neural Network, Decision Tree, Support Vector Machines are the classification tasks in the data mining. Feature selection methods involve generation of the subset, evaluation of each subset, criteria for stopping the search and validation procedures. The characteristics of the search method used are important with respect to the time efficiency of the feature selection methods. PCA (Principle Component Analysis), Information gain Subset Evaluation, Fuzzy rough set evaluation, Correlation based Feature Selection (CFS) are some of the feature selection techniques, greedy first search, ranker etc are the search algorithms that are used in the feature selection. In this paper, a new algorithm which is based on Fuzzy neural subset evaluation and artificial neural network is proposed which reduces the task of classification and feature selection separately. This algorithm combines the neural fuzzy rough subset evaluation and artificial neural network together for the better performance than doing the tasks separately.
Keywords: ANN, SVM, PCA, CFS
1) This document discusses different techniques for cross-domain data fusion, including stage-based, feature-level, probabilistic, and multi-view learning methods.
2) It reviews literature on data fusion definitions, implementations, and techniques for handling data conflicts. Common steps in data fusion are data transformation, schema mapping, and duplicate detection.
3) The proposed system architecture performs data cleaning, then applies stage-based, feature-level, probabilistic, and multi-view learning fusion methods before analyzing dataset, hardware, and software requirements.
Activity Context Modeling in Context-AwareEditor IJCATR
The explosion of mobile devices has fuelled the advancement of pervasive computing to provide personal assistance in this
information-driven world. Pervasive computing takes advantage of context-aware computing to track, use and adapt to contextual
information. The context that has attracted the attention of many researchers is the activity context. There are six major techniques that
are used to model activity context. These techniques are key-value, logic-based, ontology-based, object-oriented, mark-up schemes and
graphical. This paper analyses these techniques in detail by describing how each technique is implemented while reviewing their pros
and cons. The paper ends with a hybrid modeling method that fits heterogeneous environment while considering the entire of modeling
through data acquisition and utilization stages. The modeling stages of activity context are data sensation, data abstraction and
reasoning and planning. The work revealed that mark-up schemes and object-oriented are best applicable at the data sensation stage.
Key-value and object-oriented techniques fairly support data abstraction stage whereas the logic-based and ontology-based techniques
are the ideal techniques for reasoning and planning stage. In a distributed system, mark-up schemes are very useful in data
communication over a network and graphical technique should be used when saving context data into database.
The document summarizes three journal articles about grid and cloud computing.
The first journal investigates the benefits of grid computing technologies for high-performance computing. It uses a case study approach and experimental methodology. Three scenarios are modeled to test average job response times.
The second journal aims to develop a prototype integrating grid technologies with NASA's web GIS software. It determines integration models and system architecture through data gathering and analysis. Components are developed and tested within a virtual organization environment.
The third journal comprehensively compares grid and cloud computing concepts from different perspectives. It collects data through observations and content analysis of definitions to ensure cloud computing is not just a renaming of grid computing.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
This document proposes a new approach for processing multiple queries for a patented medical database that handles temporal domain events more efficiently. The approach uses three techniques: automatic error correction, topic relevant query suggestion, and extended query augmentation. Queries are first used to retrieve coherent clusters from topic-classified medical data. Relevant results from each cluster are then combined to generate the top K answers for the user. The techniques aim to better define the user's information need and improve retrieval speed and memory usage when searching large medical databases. An experimental evaluation found the approach improved retrieval quality and efficiency compared to existing methods.
Abstract
The exponential growth of knowledge in the World Wide Web, has understood the need to develop economical and effective ways for organizing relevant contents. In the field of web computing, document clustering plays a vital role and plays an interesting and challenging problem. Document clustering is mainly used for grouping the similar documents in the search engine. The web also has rich and dynamic collection of hyperlink information. The retrieval of relevant document from the internet is the complicated task. Based on the user’s query the document will be retrieved from the various databases to give relevant information and additional information for the given query. The documents are already clustered based on keyword extraction and stored in the database. The probabilistic relational approach for web document clustering is to find the relation between two linked pages and to define a relational clustering algorithm based on probabilistic graph representation. In document clustering, both content information and hyperlink structure of web page are considered and document is viewed as a semantic units. It also provides additional information to the user.
Keywords: Document Clustering, Agglomerative Clustering, Entropy, F-Measure
Fast Range Aggregate Queries for Big Data AnalysisIRJET Journal
The document proposes a fast range aggregate query (Fast RAQ) method to efficiently analyze large banking transaction datasets for the purpose of identifying tax violators. It divides data into partitions and generates local estimates for each partition. When a query is received, results are obtained by aggregating the local estimates from all partitions. The method is tested on banking transaction data from multiple banks partitioned and stored in Hadoop. It aims to track transactions across banks for a user using their unique ID to find individuals depositing over 50,000 rupees annually in 3 or more banks. The Fast RAQ method provides accurate results for large datasets more efficiently than existing approaches.
An Enhanced Framework for Improving Spatio-Temporal Queries for Global Positi...IJORCS
This document proposes a framework to improve the processing of spatio-temporal queries for global positioning systems. The framework employs a new indexing algorithm built on SQL Server 2008 that avoids the overhead of R-Tree indexing. It utilizes dynamic materialized views and an adaptive safe region to reduce communication costs and update loads. Caching is used to enhance performance. The notification engine processes concurrent queries using publish/subscribe to group similar queries. Experiments showed the framework outperformed R-Tree indexing.
Vertical intent prediction approach based on Doc2vec and convolutional neural...IJECEIAES
Vertical selection is the task of selecting the most relevant verticals to a given query in order to improve the diversity and quality of web search results. This task requires not only predicting relevant verticals but also these verticals must be those the user expects to be relevant for his particular information need. Most existing works focused on using traditional machine learning techniques to combine multiple types of features for selecting several relevant verticals. Although these techniques are very efficient, handling vertical selection with high accuracy is still a challenging research task. In this paper, we propose an approach for improving vertical selection in order to satisfy the user vertical intent and reduce user’s browsing time and efforts. First, it generates query embeddings vectors using the doc2vec algorithm that preserves syntactic and semantic information within each query. Secondly, this vector will be used as input to a convolutional neural network model for increasing the representation of the query with multiple levels of abstraction including rich semantic information and then creating a global summarization of the query features. We demonstrate the effectiveness of our approach through comprehensive experimentation using various datasets. Our experimental findings show that our system achieves significant accuracy. Further, it realizes accurate predictions on new unseen data.
Location-based services (LBS) provide location-aware information to mobile device users through mobile networks and location detection technologies. LBS use several components including mobile devices, positioning platforms, communication networks, service providers, and data providers. Location-aware query processing enables both snapshot queries that retrieve information based on the user's current location, as well as continuous queries that track objects as their locations change over time. Common techniques for location-aware query processing include result validation, caching, prediction, and incremental evaluation.
Data Management for Internet of things : A Survey and DiscussionIRJET Journal
This document discusses data management for the Internet of Things (IoT). It begins with an abstract that outlines the need for improved data management techniques to handle the massive volumes of data produced by IoT devices. The document then provides background on IoT data characteristics that make traditional database solutions inadequate. It surveys current research in IoT data management and proposes a framework that considers the full data lifecycle from collection to deletion. Finally, it performs a gap analysis of existing solutions based on factors like data format, storage architecture, processing speed, and server response time.
Matching GPS Traces with Personal
Schedules,” Proc. First ACM Int’l Workshop
Personalized Context Modeling and
Management for UbiComp Applications
(PCM), 2009.
[8] X. Li, Y.-Y. Chen, T. Suel, and A.
Markowetz, “Efficient Query Processing in
Geographic Web Search,” Proc. Int’l ACM
SIGIR Conf. Research and Development in
Information Retrieval (SIGIR), 2006.
[9] B.J. Jansen, A. Spink, and T. Saracevic,
“Real Life, Real Users, and Real Needs: A
Study and Analysis of User Queries
IRJET- Cluster Analysis for Effective Information Retrieval through Cohesive ...IRJET Journal
This document discusses using document clustering to improve information retrieval systems. It proposes a framework with four steps: 1) the information retrieval system retrieves documents based on a user query, 2) a similarity measure is used to determine document similarity, 3) the documents are clustered based on similarity, and 4) the clusters are ranked based on relevance to the query. The goal of clustering is to group relevant documents together to help users more easily find needed information. Different clustering algorithms are reviewed, noting that hierarchical clustering and overlapping clusters may improve search results over other methods.
This document summarizes various techniques for scalable continual top-k keyword search in relational databases. There are two main approaches: schema-based and graph-based. Schema-based methods generate candidate networks from the database schema and evaluate them. Graph-based methods represent the database as a graph and use techniques like bidirectional expansion. Top-k keyword search finds the highest scoring k results instead of all results. Methods like the Global Pipeline algorithm and Skyline-Sweeping algorithm efficiently process top-k queries over multiple candidate networks. Techniques for updating results with database changes include maintaining an initial top-k and recalculating scores. Lattice-based methods share computational costs for keyword search in data streams.
Survey on scalable continual top k keyword search in relational databaseseSAT Journals
Abstract Keyword search in relational database is a technique that has higher relevance in the present world. Extracting data from a large number of sets of database is very important .Because it reduces the usage of man power and time consumption. Data extraction from a large database using the relevant keyword based on the information needed is a very interactive and user friendly. Without knowing any database schemas or query languages like sql the user can get information. By using keyword in relational database data extraction will be simpler. The user doesn’t want to know the query language for search. But the database content is always changing for real time application for example database which store the data of publication data. When new publications arrive it should be added to database so the database content changes according to time. Because the database is updated frequently the result should change. In order to handle the database updation takes the top-k result from the currently updated data for each search. Top-k keyword search means take greatest k results based on the relevance of document. Keyword search in relational database means to find structural information from tuples from the database. Two types of keyword search are schema-based method and graph based approach. Using top-k keyword search instead of executing all query results taking highest k queries. By handling database updation try to find the new results and remove expired one
This document discusses domain specific search and ranking model adaptation using a binary classifier. It proposes using a ranking adaptation support vector machine (RA-SVM) to adapt an existing broad-based ranking model to a new targeted domain with limited labeled data. The RA-SVM utilizes predictions from the existing model as well as domain specific features to build a robust ranking model for the target domain. It evaluates the adapted model using precision, recall, average precision and Kendall's Tau metrics. The goal is to minimize the amount of labeled data needed for the target domain while still achieving good performance.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IRJET- Missing Data Imputation by Evidence ChainIRJET Journal
This document presents a new method called "evidence chain" for imputing missing data. The method works as follows:
1. It first identifies missing data values marked as "-1" in a dataset.
2. It then combines attribute values associated with each missing data point to form "evidence chains" to estimate potential values.
3. It calculates possible values and their probabilities for each missing data point.
4. It checks if an evidence chain matches values in the data and uses the most probable or highest value to impute the missing data.
5. The imputed values replace the original missing values to complete the dataset. The method is implemented in an application that can generate test data with missing values.
An effective search on web log from most popular downloaded contentijdpsjournal
A Web page recommender system effectively predicts the best related web page to search. While search
ing
a word from search engine it may display some unnecessary links and unrelated data’s to user so to a
void
this problem, the con
ceptual prediction model combines both the web usage and domain knowledge. The
proposed conceptual prediction model automatically generates a semantic network of the semantic Web
usage knowledge, which is the integration of domain knowledge and web usage i
nformation. Web usage
mining aims to discover interesting and frequent user access patterns from web browsing data. The
discovered knowledge can then be used for many practical web applications such as web
recommendations, adaptive web sites, and personali
zed web search and surfing
The document describes a proposed patent search system that aims to improve the usability of patent searches. It discusses modules for login, query processing, error correction, query suggestion, ranking results, and partitioning patents. The goal is to make the search process easier for users by correcting errors, expanding queries, and efficiently retrieving the most relevant results. Key techniques include topic modeling for suggestions, error correction using tries, and partitioning patents into groups for faster searching.
IRJET- Semantics based Document ClusteringIRJET Journal
This document describes a proposed ontology-based document clustering system. The system uses a two-step clustering algorithm that first applies K-means partitioning clustering followed by hierarchical agglomerative clustering. Ontology is introduced through a weighting scheme that integrates traditional TF-IDF word weights with weights of semantic relations between words from the ontology. The goal is to produce document clusters that are semantically meaningful by accounting for relationships between words, rather than just word co-occurrence. An overview of the system architecture and modules is provided, along with descriptions of preprocessing, concept weighting, clustering approaches, and initial implementation results.
Correlation of artificial neural network classification and nfrs attribute fi...eSAT Journals
Abstract
Mostly 5 to 15% of the women in the stage of reproduction face the disease called Polycystic Ovarian Syndrome (PCOS) which is the multifaceted, heterogeneous and complex. The long term consequences diseases like endometrial hyperplasia, type 2 diabetes mellitus and coronary disease are caused by the polycystic ovaries, chronic anovulation and hyperandrogenism are characterized with the resistance of insulin and the hypertension, abdominal obesity and dyslipidemia and hyperinsulinemia are called as Metabolic syndrome (frequent metabolic traits) The above cause the common disease called Anovulatory infertility. Computer based information along with advanced Data mining techniques are used for appropriate results. Classification is a classic data mining task, with roots in machine learning. Naïve Bayesian, Artificial Neural Network, Decision Tree, Support Vector Machines are the classification tasks in the data mining. Feature selection methods involve generation of the subset, evaluation of each subset, criteria for stopping the search and validation procedures. The characteristics of the search method used are important with respect to the time efficiency of the feature selection methods. PCA (Principle Component Analysis), Information gain Subset Evaluation, Fuzzy rough set evaluation, Correlation based Feature Selection (CFS) are some of the feature selection techniques, greedy first search, ranker etc are the search algorithms that are used in the feature selection. In this paper, a new algorithm which is based on Fuzzy neural subset evaluation and artificial neural network is proposed which reduces the task of classification and feature selection separately. This algorithm combines the neural fuzzy rough subset evaluation and artificial neural network together for the better performance than doing the tasks separately.
Keywords: ANN, SVM, PCA, CFS
1) This document discusses different techniques for cross-domain data fusion, including stage-based, feature-level, probabilistic, and multi-view learning methods.
2) It reviews literature on data fusion definitions, implementations, and techniques for handling data conflicts. Common steps in data fusion are data transformation, schema mapping, and duplicate detection.
3) The proposed system architecture performs data cleaning, then applies stage-based, feature-level, probabilistic, and multi-view learning fusion methods before analyzing dataset, hardware, and software requirements.
Activity Context Modeling in Context-AwareEditor IJCATR
The explosion of mobile devices has fuelled the advancement of pervasive computing to provide personal assistance in this
information-driven world. Pervasive computing takes advantage of context-aware computing to track, use and adapt to contextual
information. The context that has attracted the attention of many researchers is the activity context. There are six major techniques that
are used to model activity context. These techniques are key-value, logic-based, ontology-based, object-oriented, mark-up schemes and
graphical. This paper analyses these techniques in detail by describing how each technique is implemented while reviewing their pros
and cons. The paper ends with a hybrid modeling method that fits heterogeneous environment while considering the entire of modeling
through data acquisition and utilization stages. The modeling stages of activity context are data sensation, data abstraction and
reasoning and planning. The work revealed that mark-up schemes and object-oriented are best applicable at the data sensation stage.
Key-value and object-oriented techniques fairly support data abstraction stage whereas the logic-based and ontology-based techniques
are the ideal techniques for reasoning and planning stage. In a distributed system, mark-up schemes are very useful in data
communication over a network and graphical technique should be used when saving context data into database.
The document summarizes three journal articles about grid and cloud computing.
The first journal investigates the benefits of grid computing technologies for high-performance computing. It uses a case study approach and experimental methodology. Three scenarios are modeled to test average job response times.
The second journal aims to develop a prototype integrating grid technologies with NASA's web GIS software. It determines integration models and system architecture through data gathering and analysis. Components are developed and tested within a virtual organization environment.
The third journal comprehensively compares grid and cloud computing concepts from different perspectives. It collects data through observations and content analysis of definitions to ensure cloud computing is not just a renaming of grid computing.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
This document proposes a new approach for processing multiple queries for a patented medical database that handles temporal domain events more efficiently. The approach uses three techniques: automatic error correction, topic relevant query suggestion, and extended query augmentation. Queries are first used to retrieve coherent clusters from topic-classified medical data. Relevant results from each cluster are then combined to generate the top K answers for the user. The techniques aim to better define the user's information need and improve retrieval speed and memory usage when searching large medical databases. An experimental evaluation found the approach improved retrieval quality and efficiency compared to existing methods.
Abstract
The exponential growth of knowledge in the World Wide Web, has understood the need to develop economical and effective ways for organizing relevant contents. In the field of web computing, document clustering plays a vital role and plays an interesting and challenging problem. Document clustering is mainly used for grouping the similar documents in the search engine. The web also has rich and dynamic collection of hyperlink information. The retrieval of relevant document from the internet is the complicated task. Based on the user’s query the document will be retrieved from the various databases to give relevant information and additional information for the given query. The documents are already clustered based on keyword extraction and stored in the database. The probabilistic relational approach for web document clustering is to find the relation between two linked pages and to define a relational clustering algorithm based on probabilistic graph representation. In document clustering, both content information and hyperlink structure of web page are considered and document is viewed as a semantic units. It also provides additional information to the user.
Keywords: Document Clustering, Agglomerative Clustering, Entropy, F-Measure
Fast Range Aggregate Queries for Big Data AnalysisIRJET Journal
The document proposes a fast range aggregate query (Fast RAQ) method to efficiently analyze large banking transaction datasets for the purpose of identifying tax violators. It divides data into partitions and generates local estimates for each partition. When a query is received, results are obtained by aggregating the local estimates from all partitions. The method is tested on banking transaction data from multiple banks partitioned and stored in Hadoop. It aims to track transactions across banks for a user using their unique ID to find individuals depositing over 50,000 rupees annually in 3 or more banks. The Fast RAQ method provides accurate results for large datasets more efficiently than existing approaches.
An Enhanced Framework for Improving Spatio-Temporal Queries for Global Positi...IJORCS
This document proposes a framework to improve the processing of spatio-temporal queries for global positioning systems. The framework employs a new indexing algorithm built on SQL Server 2008 that avoids the overhead of R-Tree indexing. It utilizes dynamic materialized views and an adaptive safe region to reduce communication costs and update loads. Caching is used to enhance performance. The notification engine processes concurrent queries using publish/subscribe to group similar queries. Experiments showed the framework outperformed R-Tree indexing.
Vertical intent prediction approach based on Doc2vec and convolutional neural...IJECEIAES
Vertical selection is the task of selecting the most relevant verticals to a given query in order to improve the diversity and quality of web search results. This task requires not only predicting relevant verticals but also these verticals must be those the user expects to be relevant for his particular information need. Most existing works focused on using traditional machine learning techniques to combine multiple types of features for selecting several relevant verticals. Although these techniques are very efficient, handling vertical selection with high accuracy is still a challenging research task. In this paper, we propose an approach for improving vertical selection in order to satisfy the user vertical intent and reduce user’s browsing time and efforts. First, it generates query embeddings vectors using the doc2vec algorithm that preserves syntactic and semantic information within each query. Secondly, this vector will be used as input to a convolutional neural network model for increasing the representation of the query with multiple levels of abstraction including rich semantic information and then creating a global summarization of the query features. We demonstrate the effectiveness of our approach through comprehensive experimentation using various datasets. Our experimental findings show that our system achieves significant accuracy. Further, it realizes accurate predictions on new unseen data.
Location-based services (LBS) provide location-aware information to mobile device users through mobile networks and location detection technologies. LBS use several components including mobile devices, positioning platforms, communication networks, service providers, and data providers. Location-aware query processing enables both snapshot queries that retrieve information based on the user's current location, as well as continuous queries that track objects as their locations change over time. Common techniques for location-aware query processing include result validation, caching, prediction, and incremental evaluation.
Data Management for Internet of things : A Survey and DiscussionIRJET Journal
This document discusses data management for the Internet of Things (IoT). It begins with an abstract that outlines the need for improved data management techniques to handle the massive volumes of data produced by IoT devices. The document then provides background on IoT data characteristics that make traditional database solutions inadequate. It surveys current research in IoT data management and proposes a framework that considers the full data lifecycle from collection to deletion. Finally, it performs a gap analysis of existing solutions based on factors like data format, storage architecture, processing speed, and server response time.
Location Provider with Privacy Using Localized Server and GPS Editor IJCATR
Maps are an essential part of any handheld device and use constantly used for navigation and other resources by application for providing location based data which can be used for customized examination outcomes, however these data are conservatively stowed on a L.B.S Server which are susceptible to attacks and misuse as these data’s are not usually have any significant security so these data’s can be sold or misused by some other parties. We try to eliminate the problem as well as provided added functionality to the conventional maps by providing a customizable map which has the added functionality of offline use mode in addition to the online mode
Support for Goal Oriented Requirements Engineering in Elastic Cloud Applicationszillesubhan
Businesses have already started to exploit potential uses of cloud computing as a new paradigm for promoting their services. Although the general concepts they practically focus on are: viability, survivability, adaptability, etc., however, on the ground, there is still a lack for forming mechanisms to sustain viability with adaptation of new requirements in cloud-based applications. This has inspired a pressing need to adopt new methodologies and abstract models which support system acquisition for self-adaptation, thus guaranteeing autonomic cloud application behavior. This paper relies over state-of-the-art Neptune framework as runtime adaptive software development environment supported with intention-oriented modeling language in the representation and adaptation of goal based model artifacts and their intrinsic properties requirements. Such an approach will in turn support distributed service based applications virtually over the cloud to sustain a self-adaptive behavior with respect to its functional and non-functional characteristics.
Android application to locate and track mobile phones(aaltm) an implementati...eSAT Journals
Abstract
With the advancement of technology and modern science, people are expecting information about the location of any object for
tracking purposes. GPS is a system which has been implemented and is accessible without any restriction. Having the facility of
GPS, we need a GPS device to calculate the location from the information taken from GPS. This paper presents a study on
Location Based Services and is directed on locating and tracking of Android devices. In this implementation I am using Global
Positioning System (GPS) and Web Services, and android smart phone device since Android devices have a built-in feature of
GPS service. Some latest demanding tools and technology used for this implementation are Java, AVD, WAMP etc.
Keywords: Location Based Services; Global Positioning System (GPS); location; path
Managing, searching, and accessing iot devicesIJCNCJournal
In this paper a new method is proposed for management of REST-based services acting as proxies for Internet-of-Things devices. The method is based on a novel way of monitoring REST resources by hierarchical set of directories, with the possibility of smart searching for “the best” device according to atthe- place devices’ availability and functionality, overall context (including geo-location), and personal preferences. The system is resistant to changes of network addresses of the devices and their services, as well as core system points such as directories. Thus, we successfully deal with the problem of
(dis)connectivity and mobility of network nodes, and the problem of a “newcomer” device trying to connect
to the network at an incidental place/time.
Main novelty of the approach is a summary of three basic achievements. Firstly, the system introduces
unifying tools for efficient monitoring. On one hand, we may control an availability and load (statistics) of
devices/services. On the other hand, we are able to search for “the best” device/service with different criteria, also formulated ad-hoc and personalized. Secondly, the system is resistant to sudden changes of network topology and connections (basically IP addressing), and frequent disconnections of any system element, including core nodes such as central directories. As a result, we may have a common view to the whole system at any time/place and with respect to its current state, even if the elements of the system are distributed across a wider area. Thirdly, any element of the system, from simple devices to global directories, is able to self-adjust to evolving parameters of the environment (including other devices as a part of this environment). In particular, it is possible for a mobile “newcomer” device to interact with the system at any place and time without a need for prior installation, re-programming, determination of
actual parameters, etc. The presented approach is a coherent all-in-one solution to basic problems related
with efficient usage of IoT devices and services, well suited to the hardware- and software-restricted world
of Internet of Things and Services. Fully implemented, the system is now being applied for an “intelligent”
home and workplace with user-centric e-comfort management.
Efficient intelligent crawler for hamming distance based on prioritization of...IJECEIAES
Search engines play a crucial role in today's Internet landscape, especially with the exponential increase in data storage. Ranking models are used in search engines to locate relevant pages and rank them in decreasing order of relevance. They are an integral component of a search engine. The offline gathering of the document is crucial for providing the user with more accurate and pertinent findings. With the web’s ongoing expansions, the number of documents that need to be crawled has grown enormously. It is crucial to wisely prioritize the documents that need to be crawled in each iteration for any academic or mid-level organization because the resources for continuous crawling are fixed. The advantages of prioritization are implemented by algorithms designed to operate with the existing crawling pipeline. To avoid becoming the bottleneck in pipeline, these algorithms must be fast and efficient. A highly efficient and intelligent web crawler has been developed, which employs the hamming distance method for prioritizing the pages to be downloaded in each iteration. This cutting-edge search engine is specifically designed to make the crawling process more streamlined and effective. When compared with other existing methods, the implemented hamming distance method achieves a high value of 99.8% accuracy.
Use of Hidden Markov Mobility Model for Location Based ServicesIJERA Editor
These days people prefer to use portable and wireless devices such as laptops, mobile phones, They are connected through satellites. As user moves from one point to other, task of updating stored information becomes difficult. Provision of Location based services to users, faces some challenges like limited bandwidth and limited client power. To optimize data accessibility and to minimize access cost, we can store frequently accessed data item in cache of client. So small size of cache is introduced in mobile devices. Data fetched from server is stored on cache. So requested data from user is provided from cache and not from remote server. Question arises that which data should be kept in the cache? Performance of cache majorly depends on the cache replacement policies which select data suitable for eviction from cache. This paper presents use of Hidden Markov Models(HMMs) for prediction of user‟s future location. Then data item irrelevant to this predicted location is fetched out from the cache. The proposed approach clusters location histories according to their location characteristics and also it considers each user‟s previous actions. This results in producing high packet delivery ratio and minimum delay.
11.location based spatial query processing in wireless systemAlexander Decker
This document summarizes a research paper on location-based spatial query processing in wireless systems. The paper proposes a mobile application that allows users to search for places like hospitals, banks, and monuments within a given radius of their current location using GPS. It describes a system with 3-tier architecture and MVC-2 format using LWUIT for the user interface. The system returns search results ranked by distance. It aims to provide accurate and real-time responses on mobile devices for location-based queries.
11.location based spatial query processing in wireless systemAlexander Decker
This document summarizes a research paper on location-based spatial query processing in wireless systems. The paper proposes a mobile application that allows users to search for places like hospitals, banks, and monuments within a given radius of their current location using GPS. It describes a system with 3-tier architecture and MVC-2 format using LWUIT for the user interface. The system returns search results ranked by distance. It aims to provide accurate and real-time responses on mobile devices for location-based queries. Key challenges addressed are processing queries efficiently for mobile users and maintaining accuracy given wireless connectivity issues.
3.[10 13]location based spatial query processing in wireless systemAlexander Decker
This document summarizes a research paper on location-based spatial query processing in wireless systems. It discusses how spatial queries work by retrieving location-based information from a database based on a user's current location and radius. The system architecture uses a 3-tier MVC2 approach with the model for business logic, view for displaying data, and controller to manipulate the model and update the view. Key features are accuracy of location results using GPS and real-time response through optimized database and algorithm designs. The document provides details on how the system processes queries in two steps - storing location data and processing user queries within a specified radius.
Searching and Analyzing Qualitative Data on Personal ComputerIOSR Journals
This document presents the design and implementation of a desktop search system using Lucene. It describes the key components of indexing, analyzing text, storing indexes, and searching. For indexing, it discusses how documents are preprocessed, tokenized, and stored in an inverted index. For searching, it explains how queries are analyzed and the index is searched to return results. The system allows users to search for files on their personal computer. It includes a user interface to input queries and view results. Lucene provides an open-source toolkit to add full-text search capabilities to applications.
This document summarizes a research paper on developing a feature-based product recommendation system. It begins by introducing recommender systems and their importance for e-commerce. It then describes how the proposed system takes basic product descriptions as input, recognizes features using association rule mining and k-nearest neighbor algorithms, and outputs recommended additional features to improve the product profile. The paper evaluates the system's performance on recommending antivirus software features. In under 3 sentences.
11.challenging issues of spatio temporal data miningAlexander Decker
This document discusses the challenging issues of spatio-temporal data mining. It begins with an introduction to spatio-temporal databases and how they differ from traditional databases by managing moving objects and their locations over time. It then provides an overview of spatial data mining and temporal data mining before focusing on spatio-temporal data mining, which aims to analyze large databases containing both spatial and temporal information. The document outlines some of the key challenges in applying traditional data mining techniques to spatio-temporal data due to its continuous and correlated nature.
An efficient information retrieval ontology system based indexing for contexteSAT Journals
Abstract Many of the research or development projects are constructed and vast type of artifacts are released such as article, patent, report of research, conference papers, journal papers, experimental data and so on. The searching of the particular context through the keywords from the repository is not an easy task because the earliest system the problem of huge recalls with low precision. This paper challenges to construct a search algorithm based on the ontology to retrieve the relevant contexts. Ontology's are great knowledge of retrieving the context. In this paper, we utilize the WordNet ontology to retrieve the relevant contexts from the document repository. It is very difficult to retrieve the relevant context in its original format since we use the pre-processing step, which helps to retrieve context. The pre-processing step includes two major steps first one is stop word removal and the second one is stemming process. The outcome of the pre-processing step is indexing consist of important keywords and their corresponding keywords. When the user enter the keyword to the system, the ontology makes the several steps to make the refine keywords. Finally, the refine keywords are matched with index and relevant contexts are retrieved. The experimentation process is carried out with the help of different set of contexts to achieve the results and the performance analysis of the proposed approach is estimated by the evaluation metrics like precision, recall and F-measure. Keywords— Ontologies; WordNet; contexts; stemming; indexing.
Smart government transportation with cloud securityIRJET Journal
This document describes a proposed smart government transportation system that uses cloud computing and security. The system would allow users to generate passenger monthly passes online through an Android app, reducing paperwork and wait times. It uses GPS to track government vehicle locations and schedules. The system has separate logins for users and administrators. Users can renew passes and refill accounts as needed. Administrators can view user details and balances. Cloud security frameworks are used to securely maintain user data in the cloud. The system aims to provide efficient, reliable transportation services for the public.
This document summarizes a research paper that proposes a profile management system for Android mobile devices based on location. The system uses Global Positioning System (GPS) localization to detect when the mobile device reaches predefined locations and automatically changes the user's profile, such as switching to silent mode. When the device detects it is near a saved location, it calculates the distance to that location and triggers a notification and profile change. This allows profiles to be managed automatically based on the user's location rather than manually or based only on time. The paper outlines the existing solutions, proposed system design, and modules for creating profiles, accessing GPS location, and changing wallpapers and ringtones.
Similar to Mobile Location Indexing Based On Synthetic Moving Objects (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
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and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
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The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
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This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
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In structure-based techniques, most of the indexing structures and nearest neighbor searches are
separately taken by two parts: building index structure and searching nearest neighbor of the desired query
point p. In the building of a tree also includes relevant structure along with collecting and storing data points.
Then, an appropriate procedure or algorithm is used to search the nearest points from the desired query
point [6]. It is totally good for static or non-moving objects structure but it may issue in moving locations or
objects indexing. Moving object index structure with the regular update will also need to cooperate updating
of the nearest points searching algorithm. It may think consistency between index structure and searching
algorithm for discretionary data points query and search will be required concurrency control.
Recent advances in moving objects based research and applications, the positioning technologies
and indexing based structures have collaborated together thus it leads to a perfect technique for maintaining
and updating mobile position in the dynamic environment. Normally, the purpose of the traditional index
structure is generating queries faster and easier with the required information. However, a good mobile
objects' indexing needs not the only faster query but also ability to update regularly. In this paper, the
presorted-nearest index tree structure is proposed that combines the concept of the nearest neighbor algorithm
and tree-based index structure. Thus, it supports generating nearest locations by index structure from the
desired query point. In this structure, all of the location nodes are placed by level order thus nodes at any
distance can easily find without traveling the whole tree and the searching may reduce greatly.
It is harmonized to solve nearest neighbor location queries since the locations of the data points are based
only on their relative distances from each other.
In reality, the millions of mobile positioning data are unavailable for performance evaluation of
index structure. If the mobile location dataset can create synthetically, it would be free from location privacy
and confidentiality. It can be used to get location data for performance evaluation of proposed index
structure. Thus, the requirement of generating a synthetic dataset for dynamic mobile locations that seem
realistic is a challenge. Therefore, proposing a novel procedure to produce the synthetic dataset which is
based on dynamic two-dimensional points is included in this paper. According to environmental needs, many
datasets are generated synthetically such as animal, mobile user, bus, Hurricane dataset, and so on.
A synthetic dataset usually comes from application-dependent generation. Therefore, a dataset generator is
required about their domain of interest before making any dataset synthetically.
This paper explains how to process dynamic location attributes in the proposed index structure and
an appropriate process flow of mobile object generator is built to deal with the overall system. As mobile
positions are basically used in this system so that it requires continuous evaluation as the location result
needs to valid after a suitable period of time. When the use of indexing based on location or position, the
three basic positions are normally analyzed on historical, current and anticipated future position. This system
takes the second one which is based on the current position thus it has to get its location with the appropriate
update will be there. It combines Spring scheduler that automatically takes in all of the updating cases thus it
leads to reduce both server update and traffic cost [7]. The necessary performances are tested by using a
JUnit framework, which can apply to fetch and test repeatable automated tests. The results are used for
execution time, updating time and CPU usage time with the different number of mobile datasets.
This paper is described into five sections: indexing and its applied areas are introduced in section 2.
A proposed index structure, presorted-nearest index tree structure is explained in section 3. A virtual mobile
dataset generation is presented in section 4 with architecture and a process flow of mobile objects generator.
Finally, the experimental results of performance evaluations and discussions are collected in section 5.
2. INDEXING AND ITS APPLIED AREAS
Indexing is a special data structure that allows having quick access to data or objects systematically.
Indexing is usually used for users to display results that match the desired user criteria. In recent years, there
has been a survey on indexing algorithms together with the activities of moving object databases to fast and
efficient processes [8]. Normally, indexing has its own relevant structure that needs time to build it.
It is saved by building the structure only for points that need to receive information and it can be used in the
multicast notification. Nowadays, indexing has various types and fieldworks to apply in the real world. Some
types are good in querying and some are in updating. Based on the types of moving objects, such as
geographically distributed moving objects, there is a special index structure that allows generating range
monitoring queries over content-match and group-aware queries [9].
Some of the indexes preserve only for a specific type such as the trajectory of moving objects [10].
The common index for different types of query special is grid structure. With the rapid distribution of spatial
moving objects, a Distributed Grid Index (DGI) was proposed [11] that accurately interrogate the objects'
location by unique object's ID. Sometimes, the spatial index structure such as R tree used the partition of grid
index that became less overlap and gained information [12]. Like the grid structure, a quadtree index
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offered [13] better performance regardless of the amount of moving objects. However, [14] using a simple,
uniform grid index was basically supported to query processing and it is weak in update performance. In
order to better help mobile objects' query was proposed [15]. Some survey paper thought uncertain data or
object index structures were not applicable to all types of query processing [16]. As [17] discussed that some
tree-based algorithms such as R trees are not suitable for high-dimensional data in real-life applications.
Instead, they proposed Grid-index algorithm (GIR) that offers reverse rank queries with a little memory cost.
On the other hand, [18] discussed that it is hard to get an optimal resolution of index based on grid and also R
tree with the skewed mobile objects. As a result, a qualified index structure depended on the structure of
index, types of moving objects, and queries.
Some index for moving objects are based on queries regardless of moving positions, speed or
movement nature [19]. A suitable way of indexing and querying to moving objects is having a good design,
such as a spatio-temporal indexing using key-value store [20]. Due to the variation of moving objects,
the concurrency problem may occur in their indexing so that distributed index structure that exploits in
multiple machines [21]. Actually, an indexing based structure is very suitable for continuous queries within a
specified time by sending multicast messages, reports or notifications. It is more suitable as location points
are received within a range by only once the query. Without indexing, the range query is available based on
either a search according to the distance or other measures. But, it has to search along with a sequential or
order match pattern by each object in the dataset. Therefore, two issues are found in this way.
a. Such a scanning of the large dataset can be very high cost.
b. The longer dataset, the more execution time is required by sequential searching.
Especially for repeating a single range, indexing based range searching will be faster than each of
the single match of range searching. As [22] proposed an index structure called TM-RTree by planning
moving objects with different modes such as transportation, temporal and spatial. They claimed that their
proposed index structure well supported to answer queries related to the desired ranges.
3. RESEARCH METHOD
3.1. Building index structure with presorting
If the data is added by sorted list, the creation of an index is faster than the usual because it does not
have to search for the correct space to store the new value, and it saves both I/O and CPU costs. The new
value that will always be joined adjacent to the last value that was stored. The index tree would be fast as it is
easily built sequentially. If an index structure that is built by unsorted data, it might be bigger than the index
structure of the sorted data. Especially it is going to be hard to build and store in a huge amount of data size.
Ordering or sorting the data may take additional extra time, but it will be faster than any other unsorted
structure and thus it will bring to be a fast and compact index.
In this paper, a presorted-nearest index tree is proposed and it can be used for any types of two-
dimensional points. All of the incoming objects or points are ordered before building index structure. The
following algorithm is about the detailed structure of the proposed index tree.
3.2. Presorted-nearest index tree
Input c: center of location query,
R[m]: mobile locations in range lists that are sorted by latitude and longitude of each location,
m: mobile locations
Output Nearest locations index structure from center points by level order
Algorithm: 2D-Nearest Index Tree(c, R (m))
if m != null then Tree->root := c;
min_Index = 0;
Tree->leftKey: =FindNearest(c);
Tree->rightKey: =FindNearest(c);
GenerateTree(c);
FindNearest(c):
nearest := Node[0]; mindist=0;
for i=1 to R(m)
d= distance(c, nearest);
if(d[i]<d[mindist])
nearest=d[i];
return nearest;
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GenerateTree(c):
for d = 1 to height(Tree)
PrintGivenLevel (Tree, d);
if level is 1 then
print (Tree->c)
else if level>1 then
PrintGivenLevel (Tree->leftKey, level-1);
PrintGivenLevel (Tree->rightKey, level-1);
Firstly, the procedure takes mobile lists (rangeMobiles) which are in the maximum and minimum
range boundaries. The range mobiles are input as two sorted arrays. Both of these arrays are arrays of
locations which have the same location points but one is ordered by latitude and another one is ordered by
longitude. Then, the nearest location tree is built according to the CenterPoint and service range. In this
location tree, the CenterPoint take place as the root of other nodes in the tree. Then, nearest location points
are recursively located to the right and left of each root. The position of location points will be placed and
retrieved according to their level order in the tree for easy and quick to access nearest neighbor locations.
For range queries, the first thing is determining whether moving objects are in the circular range
area or not. This can easily calculate by bounding coordinates between the center and desired service
distance: (latitude, longitude, distance) in the following formula.
- minLongitude = longitude - Math.toDegrees (distance/R/Math.cos (Math.toRadians (lat)));
- maxLongitude = longitude + Math.toDegrees(distance/R/Math.cos (Math.toRadians (lat)));
- maxLatitude = latitude + Math.toDegrees(distance/R);
- minLatitude = latitude - Math.toDegrees(distance/R);
4. VIRTUAL MOBILE DATASET GENERATION
4.1. Moving object generator
In reality, different datasets have different applications and usage. Therefore, all of the researchers
should think about their domain of interest before making any dataset synthetically. It has to compare the
behaviors and functions of real data so that it seems realistic.
The required phases and detail explanations of this process bring a good idea for making any other
moving objects generator. In this architecture, synthetic mobile users' dataset is generated along with their
behaviors and activities. Practically, mobile users are generally located at stationary or no moving type,
sometimes they drive, and some are walking. Especially for updating moving data, one of the suitable forms
is updating their process based on their motions. As an example, a user who is walking and a user who is
driving will not be the same motion as the required update. The more dataset varies, the more result different,
and all of these outcomes will be used in part for incoming research. In order to better findings, virtual
mobile users are created with different forms and versions and tested by using indexing in this paper.
A suitable way that can generate synthetic dataset is deriving a model or architecture with valid
properties of real data. An appropriate and valid architecture, a moving objects generator is proposed in this
paper. The flow of the proposed moving objects generator is shown in Figure 1.
Figure 1. The architecture of mobile object generator
In each of the mobile user, different action and characteristic take different location update.
Two main steps are included in implementing this architecture. Mobile users' locations are generated
randomly within the maximum and minimum bounding coordinates that are in -90 to 90 and 0 to 180.
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Thus, the first step is generating mobile users according to the random location formula that is separately
calculated with latitude x and longitude y of each user location.
X=minX + (random ()*(maxX-minX) + 1);
Y= minY + (random ()*(maxY-minY) + 1);
Since the mobile user location has its own latitude and longitude, each of them is calculated
separately. After getting random locations, all of these are divided by three types of mobile users who are
walking, driving and stationary according to the time and distance threshold values along with their updates
are done for each. At the second step, a location update policy is drawn as mobile users' behaviors and
actions are quite different. In this step, time and distance based location update strategies are used to separate
mobile users' types. The output will be a synthetic dataset that includes finding locations and updating them
appropriately.
4.2. Process flow of generating mobile dataset
The process flow of the proposed architecture on moving objects generator is clearly explained in
Figure 2.
Figure 2. Process flow of mobile objects generator
The first phase is to find the location of mobile users synthetically because it is hard to get the
millions of mobile locations in reality and it is also a way of privacy protection for real world. After getting
these locations of mobile users, all of these locations are loaded from database to memory. Then, the
following two steps are done by Spring scheduler that runs with the help of Spring Boot at the specific time.
The required update locations are done by using the following formula.
Update Lat =.asin (sin (latitude)*cos (UpdateDistance/R) + cos (latitude)*sin (UpdateDistance/R)*
cos(BearingAngle));
Update Lon = longitude+atan2 (sin(BearingAngle)*sin(UpdateDistance/R)*cos(latitude),cos(Update
Distance /R)-sin(latitude)* sin(updateLat));
Firstly, mobile users are taken out of memory before doing any process. Second, mobile users'
locations are updated by moving types after checking mobile user lists which are available for processing.
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5. EXPERIMENTAL RESULTS
We managed an experimental setting on a computer with an Intel Core i7-4590U CPU, 8G RAM,
and 1-TB hard disk storage. We applied the proposed synthetic dataset generator for mobile locations to
simulate moving objects that basically came from the random function of three different behaviors such as
the car, walking and stationary. All of the activations of this generator are done by spring boot [17]. It is
designed chastely for all applications based on spring development. Its important concepts are automatic
configuration, starter dependences, and the command line interpreter. Spring boot provides fast and wide
accesses for all development and provides non-functional features.
In order to get the performance evaluations, we tested about proposed index tree construction, its
range queries and nearest neighbor queries with the comparison of using KD tree. Moreover, CPU time is
calculated and noted with the increasing number of mobiles. To improve performance evaluations, we clearly
showed the tested results by different range values and number of mobiles. The required parameters and their
values for performance evaluations are described in Table 1.
Table 1. Terms and values
Parameters Values
Synthetic Mobile Generation Range Min Latitude 9.6, Max Latitude 28.5
Min Longitude 92.2, Max Longitude 101.17
Number of Objects 1000 – 100,000
Object Types Car, Walking, Stationary
Bearing Angles 0.
– 360.
Update Distance 0.00832km,0.01112km,0.0102km
Schedule Fixed Rate
(for Random Mobile Users)
2000msec
Schedule Fixed Rate
(for Building Tree)
10000msec
Initial Delay 5000msec
5.1. Tree construction time comparsion
The Figure 3 describes the results of the experiment on presorted-nearest index tree and KD tree
over tree construction time. For this comparison, the results are confirmed after testing an average of 15
times with the dataset is from 1000 to 10,000 mobile locations. In this experiment, the number of mobiles is
generated by proposed generator which is initialized by adding the same number of moving object types. It
can be concluded that the proposed tree construction is one third faster than KD tree. This is because both
comparative trees are unbalancing two-dimensional trees and the proposed tree input is being included the
order queries that provide to be fast in tree construction. In addition, the form of the proposed tree is already
out of their nearest neighbor by level order since tree construction.
5.2. Range searching Time Comparsion
The Figure 4 shows the test for the duration of searching time within a distance of 100 kilometers to
600 kilometers range search queries. This experiment includes a dataset 10000 and a center latitude and
longitude that take as University of Computer Studies, Yangon GPS coordinate. It was not described the
tested results within a distance of less than 100 km range searching time because it was taken nearly the same
in (1 millisecond). According to the figure, both KD tree and presorted-nearest index tree are adequately
supportive to range searching.
5.3. Comparison of Nearest Neighbor Search within a Range
The Figure 5 shows the comparison results of the presorted-nearest index tree and KD tree that acts
within a range of mobile locations from the nearest are marked and calculated.. In this experiment, the
circular range is used for finding range search. To undertake the duration of the experiment, the mobile
locations are firstly checked within the range or not, which has marked the time. Then, the displaying the
mobile user lists by nearest called nearest neighbor search is tested and also marked the time. Such a query
can be used for not only static or stationary locations but also moving the location query that repeatedly
undertakes as a continuous range query. According to the experimental results, the presorted-nearest index
tree execution takes a half time in the execution of KD tree. This is because although the searching time over
two comparison trees are nearly the same, the structure of presorted-nearest index tree is already supported to
nearest neighbor within the tree construction thus it can search for nearest neighbor within one millisecond.
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Figure 3. Processing and comparison
of constructing time
Figure 4. Evaluation of range searching time
Figure 5. Execution time between proposed tree and KD tree
6. DISCUSSION
The calculation of the performance evaluation over two non-balanced trees (presorted-nearest index
tree and KD tree) is based on the tree construction time, execution time during range searches and nearest
neighbor searches. In this calculation, the generating of the latitude and longitude which is the source of the
synthetic mobile dataset is taken by Myanmar country boundaries within the maximum and minimum
latitudes and longitudes along with the proposed formula to retrieve and update. One prominent characteristic
within tests found that the proposed tree (presorted-nearest index tree) is faster in tree building due to ordered
data by queries as the input data. It can be found that both comparative trees (presorted-nearest index tree and
KD tree) are conveniently and fast in the range search without delay. Besides, the proposed tree provided the
nearest neighbor by level order since the tree building so that it does not need to give private time in the
nearest neighbor searching.
7. CONCLUSION
This paper emphasized an efficient indexing and querying structure of mobile objects on a synthetic
dataset. According to the difficulties of getting millions of mobile locations, a synthetic mobile dataset model
is proposed along with the required framework and its process flowchart. The presorted-nearest index tree is
proposed that systematically addressed and updated the locations of mobile and supported to desired range
queries over mobile objects. Experimental results indicate the efficiency of using proposed index tree is more
than using the KD tree structure for moving objects. The variation of mobile objects is handled by presorting
in the proposed tree structure. However, the proposed structure which is presorted by input queries resist the
skewed distributions of mobile objects. Moreover, the proposed structure performed the desired range queries
and nearest neighbor search within the short execution time, especially in the large mobile dataset.
ACKNOWLEDGEMENTS
I would like to express very special thanks to my supervisor Dr. Sabai Phyu, Professor, the
University of Computer Studies, Yangon. I would not be able to continue without her supervision and
valuable guidance during period of the research study.
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BIOGRAPHIES OF AUTHORS
Thu Thu Zan is a research candidate at Cloud Computing Lab, University of Computer Studies
Yangon. Her current research is based on generating synthetic dataset for moving objects, wireless
network operation and cloud computing. She is also a member of IAENG (International Association of
Engineer).
Sabai Phyu is a dean of Cloud Computing Lab at University of Computer Studies, Yangon (UCSY)
Myanmar. She is interested in virtualization and cloud computing. She currently works at the Software
Department, UCSY Myanmar.