This document discusses algorithms for efficiently processing top-k spatial preference queries in databases containing spatial and non-spatial data. It defines top-k spatial preference queries as ranking objects based on quality and features in their nearest neighborhoods. It presents the branch and bound and feature join algorithms for computing the top-k results without having to calculate scores for all objects. It also discusses using R-trees to index spatial data and feature data to accelerate query processing.
Ranking spatial data by quality preferences pptSaurav Kumar
A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example, using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital, market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to assign higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of our methods on both real and synthetic data reveals that an optimized branch-and-bound solution is efficient and robust with respect to different parameters
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Aggregation of data by using top k spatial query preferencesAlexander Decker
This document summarizes a research paper on efficient techniques for processing top-k spatial preference queries in a database. It discusses how such queries allow users to rank spatial objects based on the aggregated qualities of nearby features. It proposes two algorithms - branch-and-bound and feature join - to efficiently process these queries by pruning search space. The paper also studies extensions of the algorithms for different aggregate functions and for queries using an influence score to weight nearby features.
This document proposes and defines a top-k spatial preference query that ranks spatial objects based on the qualities of features in their neighborhood. It introduces existing systems that do not support this type of query. The proposed system uses a scoring function to calculate a score for each object based on features within its neighborhood, defined by either a range score or influence score. It describes algorithms to perform a multiway join on feature trees to obtain qualified feature combinations and search the object tree to retrieve relevant objects.
The document summarizes research on performing spatio-textual similarity joins. It discusses:
1) Developing a filter-and-refine framework to efficiently find similar object pairs from two datasets using signatures.
2) Generating spatial and textual signatures for objects and building inverted indexes on the signatures to find candidate pairs.
3) Refining the candidate pairs to obtain the final result pairs that satisfy spatial and textual similarity thresholds.
Scalable Keyword Cover Search using Keyword NNE and Inverted IndexingIRJET Journal
This document discusses scalable keyword cover search using keyword nearest neighbor expansion (keyword-NNE) and inverted indexing. It proposes a more efficient algorithm called keyword-NNE to address the performance issues of existing baseline algorithms for closest keyword search as the number of query keywords increases. Keyword-NNE significantly reduces the number of candidate keyword covers generated compared to the baseline. The algorithm and inverted indexing techniques are analyzed and shown to outperform alternatives through experiments on real datasets.
Spatial databases are used to store geographic information. Querying on such databases are : range queries, nearest neighbor queries and spatial joins. Many indexing techniques are used for faster retrieval of data out of which r-trees are mainly efficient. Other indexing techniques are quad-trees, grid files etc. Spatial data is used in GIS applications.
Spatial databases allow for storage and querying of spatial data types like points, lines, and regions. A spatial database management system (SDBMS) extends a regular DBMS with spatial data types, models, and query languages. It supports spatial indexing and algorithms for spatial queries. Common spatial queries include selection queries to find objects within a region, nearest neighbor queries to find closest objects, and spatial joins to find object relationships. Spatial indexing uses access methods like R-trees that represent regions as minimum bounding rectangles to enable efficient filtering and refinement of query results.
Ranking spatial data by quality preferences pptSaurav Kumar
A spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example, using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital, market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to assign higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of our methods on both real and synthetic data reveals that an optimized branch-and-bound solution is efficient and robust with respect to different parameters
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Aggregation of data by using top k spatial query preferencesAlexander Decker
This document summarizes a research paper on efficient techniques for processing top-k spatial preference queries in a database. It discusses how such queries allow users to rank spatial objects based on the aggregated qualities of nearby features. It proposes two algorithms - branch-and-bound and feature join - to efficiently process these queries by pruning search space. The paper also studies extensions of the algorithms for different aggregate functions and for queries using an influence score to weight nearby features.
This document proposes and defines a top-k spatial preference query that ranks spatial objects based on the qualities of features in their neighborhood. It introduces existing systems that do not support this type of query. The proposed system uses a scoring function to calculate a score for each object based on features within its neighborhood, defined by either a range score or influence score. It describes algorithms to perform a multiway join on feature trees to obtain qualified feature combinations and search the object tree to retrieve relevant objects.
The document summarizes research on performing spatio-textual similarity joins. It discusses:
1) Developing a filter-and-refine framework to efficiently find similar object pairs from two datasets using signatures.
2) Generating spatial and textual signatures for objects and building inverted indexes on the signatures to find candidate pairs.
3) Refining the candidate pairs to obtain the final result pairs that satisfy spatial and textual similarity thresholds.
Scalable Keyword Cover Search using Keyword NNE and Inverted IndexingIRJET Journal
This document discusses scalable keyword cover search using keyword nearest neighbor expansion (keyword-NNE) and inverted indexing. It proposes a more efficient algorithm called keyword-NNE to address the performance issues of existing baseline algorithms for closest keyword search as the number of query keywords increases. Keyword-NNE significantly reduces the number of candidate keyword covers generated compared to the baseline. The algorithm and inverted indexing techniques are analyzed and shown to outperform alternatives through experiments on real datasets.
Spatial databases are used to store geographic information. Querying on such databases are : range queries, nearest neighbor queries and spatial joins. Many indexing techniques are used for faster retrieval of data out of which r-trees are mainly efficient. Other indexing techniques are quad-trees, grid files etc. Spatial data is used in GIS applications.
Spatial databases allow for storage and querying of spatial data types like points, lines, and regions. A spatial database management system (SDBMS) extends a regular DBMS with spatial data types, models, and query languages. It supports spatial indexing and algorithms for spatial queries. Common spatial queries include selection queries to find objects within a region, nearest neighbor queries to find closest objects, and spatial joins to find object relationships. Spatial indexing uses access methods like R-trees that represent regions as minimum bounding rectangles to enable efficient filtering and refinement of query results.
TYBSC IT PGIS Unit II Chapter I Data Management and Processing SystemsArti Parab Academics
This document discusses geographic information systems (GIS). It defines GIS as hardware and software used to process, store, and transfer geographic data. It describes how GIS has evolved from using analog data and manual processing to increased use of digital data, computers, and software. It also discusses key GIS concepts like spatial data capture and analysis, data storage and management, and data presentation.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Spatial databases are designed to store and analyze spatial data more efficiently than traditional databases. Spatial data represents objects in geometric space and includes points, lines, and polygons. Spatial databases use spatial indexes and spatial query languages to optimize storage and retrieval of spatial data types and allow spatial queries and analysis. Common spatial database operations include measurements, functions, and predicates on geometric objects.
Data Entry and Preparation Spatial Data Input: Direct spatial data capture, Indirect spatial data captiure, Obtaining spatial data elsewhere Data Quality: Accuracy and Positioning, Positional accuracy, Attribute accuracy, Temporal accuracy, Lineage, Completeness, Logical consistency Data Preparation: Data checks and repairs, Combining data from multiple sources Point Data Transformation: Interpolating discrete data, Interpolating continuous data
TYBSC IT PGIS Unit I Chapter I- Introduction to Geographic Information SystemsArti Parab Academics
A Gentle Introduction to GIS The nature of GIS: Some fundamental observations, Defining GIS, GISystems, GIScience and GIApplications, Spatial data and Geoinformation. The real world and representations of it: Models and modelling, Maps, Databases, Spatial databases and spatial analysis
This document discusses various GIS analysis techniques including buffering, overlay, and distance measurement. Buffering creates zones around features, which can be used for planning purposes like restricting certain land uses near schools. Overlay combines data layers by joining features that occupy the same space. Feature overlay splits features where they overlap, while raster overlay combines characteristics across layers. Overlay is useful for queries and modeling. Distance measurement finds straight-line distances between features for analysis and pattern detection.
The document provides an overview of spatial databases and geographic information systems (GIS). It discusses key concepts such as spatial data types, spatial queries, and spatial database management systems. It also presents three case studies that demonstrate real-world applications of spatial databases and GIS for tasks such as predicting wetland bird nest locations, estimating greenhouse gas emissions from land use changes, and assessing land use changes in the Pantanal region of Brazil over time. Overall, the document outlines how spatial databases and GIS can store, analyze, and help understand spatial data and spatial relationships.
Spatial Database and Database Management SystemLal Mohammad
This document discusses spatial databases and database management systems. It defines spatial data as data related to location, such as coordinates, and non-spatial data as descriptive attributes not defined by geometry alone, such as area or name. A spatial database is optimized to store and query spatial data using spatial indexes, queries, analysis and intelligence. A database management system allows users to define, construct and manipulate databases for applications through components like storage management and query processing. Different database models - hierarchical, network, relational and object-oriented - are described based on how data and relationships are represented.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
This document discusses integrated analysis of spatial and non-spatial data in geographic information systems (GIS). It defines GIS and describes how spatial data includes location, shape and size attributes and can be represented as rasters or vectors, while non-spatial data provides additional information linked to spatial features. The document outlines common spatial analysis functions like overlay and neighborhood analysis that integrate both data types to answer questions about real-world features and relationships.
- The document describes a study that used various machine learning algorithms to classify images of letters based on 16 extracted features.
- The best performing algorithm was the Gaussian Mixture Model, which achieved 96.43% accuracy by modeling each letter as a Gaussian distribution and accounting for correlations between features.
- The k-Nearest Neighbors algorithm achieved 95.65% accuracy when using only the single nearest neighbor for classification, while the Naive Bayes classifier achieved only 62.45% due to its strong independence assumptions.
This document provides an overview of concepts and functions of geographic information systems (GIS). It discusses how GIS is an information system that uses spatial data and modeling. It also describes the key functionalities of GIS, including input, query, display, analysis, and management of geospatial data and transformations. Databases that store geospatial data are managed through database management systems to allow for definition, updating, retrieval, and other functions for both vector and raster data in GIS.
The document summarizes a seminar on spatial databases. It defines spatial data as data that identifies geographic locations and features on Earth. It then defines a spatial database as a database optimized to store and query geometric objects like points, lines and polygons. It lists some key advantages of spatial databases like transactions, security and concurrent users. It also lists some disadvantages as being complex to design and requiring suitable hardware. It concludes that spatial databases are valuable for applications like military planning, insurance and healthcare by allowing storage, querying and sharing of spatial data.
The document proposes an improved clustering algorithm for social network analysis. It combines BSP (Business System Planning) clustering with Principal Component Analysis (PCA) to group social network objects into classes based on their links and attributes. Specifically, it applies PCA before BSP clustering to reduce the dimensionality of the social network data and retain only the most important variables for clustering. This improves the BSP clustering results by focusing on the key information in the social network.
Spatial analysis and Analysis Tools ( GIS )designQube
This document discusses various analysis tools for working with geographic data. It covers tools for map algebra, mathematics, multi-variate analysis, neighborhoods, rasters, reclassifying rasters, solar radiation, and surfaces. The tools allow for exploring relationships between attributes, combining raster bands, clustering analysis, weighted overlays, statistics on raster neighborhoods, creating constant, normal and random rasters, hillshading, slope, curvature, contours and calculating surface volumes.
Data Allocation Strategies for Leakage DetectionIOSR Journals
This document summarizes data allocation strategies for detecting data leakage when sensitive data is distributed through trusted agents. It proposes injecting "fake but genuine-looking" data along with real data to improve the ability to detect if leakage occurs and identify the agent responsible. Various allocation algorithms are presented, including random selection and maximizing the difference between agents' probabilities of guilt. Empirical results show the average detection metric is improved when fake data is used compared to no fake data. The strategies aim to detect leakage without modifying the original data, unlike traditional watermarking techniques.
Adapting Software Components Dynamically for the GridIOSR Journals
This document summarizes a research paper on dynamically adapting software components for grid computing. It presents a framework for dynamic adaptation that incorporates a layered architecture with concepts of dynamicity. The framework separates adaptation mechanisms from component content. It defines four steps for adaptation: observe the execution environment, decide if adaptation is needed, plan adaptation actions, and execute planned actions. The framework uses three levels - a functional level for component services, a component-independent level for adaptation mechanisms, and a component-specific level for developer customization. It evaluates using this framework to design dynamically adaptable scientific application components.
TYBSC IT PGIS Unit II Chapter I Data Management and Processing SystemsArti Parab Academics
This document discusses geographic information systems (GIS). It defines GIS as hardware and software used to process, store, and transfer geographic data. It describes how GIS has evolved from using analog data and manual processing to increased use of digital data, computers, and software. It also discusses key GIS concepts like spatial data capture and analysis, data storage and management, and data presentation.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Spatial databases are designed to store and analyze spatial data more efficiently than traditional databases. Spatial data represents objects in geometric space and includes points, lines, and polygons. Spatial databases use spatial indexes and spatial query languages to optimize storage and retrieval of spatial data types and allow spatial queries and analysis. Common spatial database operations include measurements, functions, and predicates on geometric objects.
Data Entry and Preparation Spatial Data Input: Direct spatial data capture, Indirect spatial data captiure, Obtaining spatial data elsewhere Data Quality: Accuracy and Positioning, Positional accuracy, Attribute accuracy, Temporal accuracy, Lineage, Completeness, Logical consistency Data Preparation: Data checks and repairs, Combining data from multiple sources Point Data Transformation: Interpolating discrete data, Interpolating continuous data
TYBSC IT PGIS Unit I Chapter I- Introduction to Geographic Information SystemsArti Parab Academics
A Gentle Introduction to GIS The nature of GIS: Some fundamental observations, Defining GIS, GISystems, GIScience and GIApplications, Spatial data and Geoinformation. The real world and representations of it: Models and modelling, Maps, Databases, Spatial databases and spatial analysis
This document discusses various GIS analysis techniques including buffering, overlay, and distance measurement. Buffering creates zones around features, which can be used for planning purposes like restricting certain land uses near schools. Overlay combines data layers by joining features that occupy the same space. Feature overlay splits features where they overlap, while raster overlay combines characteristics across layers. Overlay is useful for queries and modeling. Distance measurement finds straight-line distances between features for analysis and pattern detection.
The document provides an overview of spatial databases and geographic information systems (GIS). It discusses key concepts such as spatial data types, spatial queries, and spatial database management systems. It also presents three case studies that demonstrate real-world applications of spatial databases and GIS for tasks such as predicting wetland bird nest locations, estimating greenhouse gas emissions from land use changes, and assessing land use changes in the Pantanal region of Brazil over time. Overall, the document outlines how spatial databases and GIS can store, analyze, and help understand spatial data and spatial relationships.
Spatial Database and Database Management SystemLal Mohammad
This document discusses spatial databases and database management systems. It defines spatial data as data related to location, such as coordinates, and non-spatial data as descriptive attributes not defined by geometry alone, such as area or name. A spatial database is optimized to store and query spatial data using spatial indexes, queries, analysis and intelligence. A database management system allows users to define, construct and manipulate databases for applications through components like storage management and query processing. Different database models - hierarchical, network, relational and object-oriented - are described based on how data and relationships are represented.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
This document discusses integrated analysis of spatial and non-spatial data in geographic information systems (GIS). It defines GIS and describes how spatial data includes location, shape and size attributes and can be represented as rasters or vectors, while non-spatial data provides additional information linked to spatial features. The document outlines common spatial analysis functions like overlay and neighborhood analysis that integrate both data types to answer questions about real-world features and relationships.
- The document describes a study that used various machine learning algorithms to classify images of letters based on 16 extracted features.
- The best performing algorithm was the Gaussian Mixture Model, which achieved 96.43% accuracy by modeling each letter as a Gaussian distribution and accounting for correlations between features.
- The k-Nearest Neighbors algorithm achieved 95.65% accuracy when using only the single nearest neighbor for classification, while the Naive Bayes classifier achieved only 62.45% due to its strong independence assumptions.
This document provides an overview of concepts and functions of geographic information systems (GIS). It discusses how GIS is an information system that uses spatial data and modeling. It also describes the key functionalities of GIS, including input, query, display, analysis, and management of geospatial data and transformations. Databases that store geospatial data are managed through database management systems to allow for definition, updating, retrieval, and other functions for both vector and raster data in GIS.
The document summarizes a seminar on spatial databases. It defines spatial data as data that identifies geographic locations and features on Earth. It then defines a spatial database as a database optimized to store and query geometric objects like points, lines and polygons. It lists some key advantages of spatial databases like transactions, security and concurrent users. It also lists some disadvantages as being complex to design and requiring suitable hardware. It concludes that spatial databases are valuable for applications like military planning, insurance and healthcare by allowing storage, querying and sharing of spatial data.
The document proposes an improved clustering algorithm for social network analysis. It combines BSP (Business System Planning) clustering with Principal Component Analysis (PCA) to group social network objects into classes based on their links and attributes. Specifically, it applies PCA before BSP clustering to reduce the dimensionality of the social network data and retain only the most important variables for clustering. This improves the BSP clustering results by focusing on the key information in the social network.
Spatial analysis and Analysis Tools ( GIS )designQube
This document discusses various analysis tools for working with geographic data. It covers tools for map algebra, mathematics, multi-variate analysis, neighborhoods, rasters, reclassifying rasters, solar radiation, and surfaces. The tools allow for exploring relationships between attributes, combining raster bands, clustering analysis, weighted overlays, statistics on raster neighborhoods, creating constant, normal and random rasters, hillshading, slope, curvature, contours and calculating surface volumes.
Data Allocation Strategies for Leakage DetectionIOSR Journals
This document summarizes data allocation strategies for detecting data leakage when sensitive data is distributed through trusted agents. It proposes injecting "fake but genuine-looking" data along with real data to improve the ability to detect if leakage occurs and identify the agent responsible. Various allocation algorithms are presented, including random selection and maximizing the difference between agents' probabilities of guilt. Empirical results show the average detection metric is improved when fake data is used compared to no fake data. The strategies aim to detect leakage without modifying the original data, unlike traditional watermarking techniques.
Adapting Software Components Dynamically for the GridIOSR Journals
This document summarizes a research paper on dynamically adapting software components for grid computing. It presents a framework for dynamic adaptation that incorporates a layered architecture with concepts of dynamicity. The framework separates adaptation mechanisms from component content. It defines four steps for adaptation: observe the execution environment, decide if adaptation is needed, plan adaptation actions, and execute planned actions. The framework uses three levels - a functional level for component services, a component-independent level for adaptation mechanisms, and a component-specific level for developer customization. It evaluates using this framework to design dynamically adaptable scientific application components.
Credit Card Duplication and Crime Prevention Using BiometricsIOSR Journals
1. The document proposes using iris recognition and palm vein technology for credit card authentication as a way to improve security over existing methods.
2. Current authentication methods like PINs, signatures, and fingerprints have vulnerabilities like being observable and reproducible.
3. The proposed system uses iris recognition followed by palm vein scanning, comparing the biometric data to stored templates to authenticate the user. If both comparisons match, the transaction would be allowed.
4. Iris patterns and palm vein patterns are unique to each individual and difficult to reproduce, providing improved security over existing authentication methods.
Compromising windows 8 with metasploit’s exploitIOSR Journals
1. The document discusses compromising a Windows 8 system using Metasploit's exploits. It performs penetration testing on a virtual machine with Windows 8 and Backtrack Linux.
2. It uses Metasploit to generate a reverse_tcp meterpreter payload executable, but first makes it fully undetectable using PEScrambler to evade the Windows 8 malware protection.
3. The payload is deployed using the ms08_067_netapi exploit, giving a meterpreter session. Privileges are escalated by migrating to the explorer process and accessing the C drive to establish a persistent backdoor.
Introducing a Gennext Banking with a Direct Banking SolutionIOSR Journals
This document introduces a proposed direct banking solution as a next generation banking model. It begins with an overview of the increasing demands from digital customers and the need for banks to optimize services and minimize costs. It then discusses current banking technologies like ATMs, internet banking, and core banking solutions. However, it notes that existing solutions are still employee-centric rather than customer-centric. The proposed direct banking solution aims to provide personalized, anytime/anywhere banking through various delivery channels including ATMs, telebanking, online/mobile banking, social media, smartphones, and television. It outlines the key components of the direct banking architecture including business processes, services, and integration with core banking. Benefits of this branchless banking model include increased
Quality of Service Optimization in Realm of Green Monitoring using Broad Area...IOSR Journals
This document discusses optimizing quality of service in wireless sensor networks using broad area sensor networks (BASN). It describes how BASN allows for real-time communication between sensor nodes and a base station, avoiding delays from routing through multiple nodes. The document provides an overview of typical mesh network topology in wireless sensor networks and its limitations. It then introduces the concept of BASN, which uses long-range wireless transceivers to allow sensor nodes to transmit directly to a base station kilometers away. This enables applications requiring low node density over a large area or real-time data transmission. The feasibility of a BASN is evaluated through a case study comparing energy generation from solar cells and storage in supercapacitors to transmission energy needs for long
The document describes the cardiovascular system and cardiovascular emergencies. It discusses the components of the cardiovascular system including the heart, vessels, and blood. It describes the pathways of blood flow through the systemic and pulmonary circulations. It then covers abnormal heart conditions like angina, myocardial infarction, and congestive heart failure. It also discusses vascular emergencies such as atherosclerosis, thrombus, embolism, and aneurysm.
1) Shock results from inadequate blood flow (perfusion) to tissues, depriving cells of oxygen and energy. Cardiogenic, hypovolemic, neurogenic, septic, anaphylactic, and psychogenic shock can all cause this.
2) Signs of shock include restlessness, decreased consciousness, rapid breathing, pale skin, and a weak pulse. Internal bleeding can also cause shock and is suggested by pain at injury sites or bleeding from body openings.
3) Treatment for shock controls external bleeding, provides oxygen, and in some cases raises the legs; nothing is given by mouth. Internal bleeding requires rapid transport to a hospital.
A Review of FPGA-based design methodologies for efficient hardware Area estim...IOSR Journals
This document reviews different FPGA-based design methodologies and optimization techniques that can be used for efficient hardware area estimation. It discusses the standard FPGA design flow including capturing the design, logic synthesis, technology mapping, placement, routing and bitstream generation. It then reviews several area estimation techniques such as resource sharing, proper reset strategies, optimizing for speed, and targeting specific FPGA technologies. Several papers are cited that propose techniques for fast area estimation, hardware/software partitioning for FPGAs, estimating multipliers and DSP blocks, and estimating designs captured using different languages. The document concludes that factors like FPGA architecture selection, design methodology, and optimization techniques play an important role in efficient FPGA-based design.
Efficiency of TreeMatch Algorithm in XML Tree Pattern MatchingIOSR Journals
This document discusses and compares the efficiency of different algorithms for matching XML tree patterns, including TwigStack, OrderedTJ, TJFast, and TreeMatch. It analyzes the performance of each algorithm using example queries and datasets. The key points are:
- TwigStack outputs many intermediate results that are not useful for the final answers when queries contain parent-child relationships.
- OrderedTJ scales linearly with database size and outputs fewer useless results than TwigStack, but is not optimal.
- The document evaluates the performance of the algorithms and concludes by discussing which algorithm is superior.
Use Email Marketing wisely; Stand out from Junk Mailbelieve52
Email marketing is an effective but cheap way to promote products, however junk mail is common so marketers must stand out. The document provides tips for effective email marketing: 1) use text rather than images to avoid being marked as spam, 2) avoid attachments which can spread viruses, 3) avoid fancy HTML which may not display properly on all email clients, 4) address the recipient by name to increase interest, and 5) provide a valid sender address to avoid being blocked or marked as spam.
This document describes a study that aimed to develop and characterize an adsorbent from rice husk ash to bleach vegetable oils such as palm, palm kernel, and groundnut oils. Rice husk samples were pretreated with different concentrations of HCl and then calcined at 600°C for 3 hours. The optimum conditions were determined to be pretreatment with 2.5M HCl and calcination at 600°C for 3 hours. Under these conditions, the rice husk ash showed the best bleaching potential for palm kernel and palm oils with 2.5M HCl and for groundnut oil with 2M HCl. Characterization of the rice husk ash samples found that acid pretreatment improved the bleaching
Generate an Encryption Key by using Biometric Cryptosystems to secure transfe...IOSR Journals
The document describes a proposed method for generating an encryption key from biometric cryptosystems to securely transfer data over a network. It involves extracting minutiae points from a fingerprint scan, generating a cryptographic key from the biometric template, and using an RSA encryption algorithm with the biometric-derived private key. A public key is also calculated based on ridge and furrow patterns in the fingerprint scan. The goal is to uniquely generate encryption keys for each individual using their biometric fingerprint information to add an extra layer of security beyond traditional encryption techniques.
Remedyto the Shading Effect on Photovoltaic CellIOSR Journals
This document discusses remedies for the shading effect on photovoltaic cells. It proposes connecting bypass diodes parallel to solar cells such that when shading occurs, the reverse voltage enables the bypass diode to conduct current from the unshaded cells. This results in the current from the unshaded cells flowing through the bypass diode, showing a second local maximum on the power/voltage characteristics. The shaded cell is only loaded with power from the unshaded cells in that section. The document then provides details on sizing the components of a photovoltaic system based on load assessment, including selecting a 1.5 kVA inverter, a 24V 400Ah battery bank, and determining the required solar panel size
SIMESITEM 2012: The European ARtSENSE project: Towards the Adaptive Augmented...ARtSENSE_EU
The document outlines presentations from the ARtSense Consortium meeting which discussed projects using augmented reality for cultural heritage applications, including the ARCHEOGUIDE project, augmented reality systems for museum guidance, and plans to use augmented reality to bring artifacts like Lavoisier's laboratory to life for visitors in a way that provides historical and technical context. Museum professionals, artists, and technologists discussed challenges and opportunities in developing augmented reality experiences for museums and cultural institutions.
Enabling Use of Dynamic Anonymization for Enhanced Security in CloudIOSR Journals
This document summarizes various techniques for anonymizing data to protect privacy and security when data is stored in the cloud. It discusses how anonymization removes identifying attributes from data to prevent individuals from being identified. The document reviews existing anonymization models like k-anonymity, l-diversity and t-closeness. It then describes different anonymization techniques like hashing, hiding, permutation, shifting, truncation, prefix-preserving and enumeration that were implemented to anonymize data fields. The goal is to anonymize data in a way that balances privacy, security, and the ability to still use the data for appropriate purposes.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
This document summarizes two algorithms - MFA and ATRA - for processing top-k spatial preference queries. MFA is a threshold-based algorithm that partitions queries into three features - spatial, preference, and text - and retrieves objects with the highest aggregate scores. ATRA uses a hybrid indexing structure called AIR-tree to more efficiently retrieve only relevant objects without revisiting the same data. The paper then proposes using an R-tree index structure combined with an enhanced branch-and-bound search algorithm to answer preference-based top-k spatial keyword queries by ranking objects based on feature quality in their neighborhoods.
Convincing a customer is always considered as a challenging task in every business. But when it comes to online business, this task becomes even more difficult. Online retailers try everything possible to gain the trust of the customer. One of the solutions is to provide an area for existing users to leave their comments. This service can effectively develop the trust of the customer however normally the customer comments about the product in their native language using Roman script. If there are hundreds of comments this makes difficulty even for the native customers to make a buying decision. This research proposes a system which extracts the comments posted in Roman Urdu, translate them, find their polarity and then gives us the rating of the product. This rating will help the native and non-native customers to make buying decision efficiently from the comments posted in Roman Urdu.
Spatial database are becoming more and more popular in recent years. There is more and more
commercial and research interest in location-based search from spatial database. Spatial keyword search
has been well studied for years due to its importance to commercial search engines. Specially, a spatial
keyword query takes a user location and user-supplied keywords as arguments and returns objects that are
spatially and textually relevant to these arguments. Geo-textual index play an important role in spatial
keyword querying. A number of geo-textual indices have been proposed in recent years which mainly
combine the R-tree and its variants and the inverted file. This paper propose new index structure that
combine K-d tree and inverted file for spatial range keyword query which are based on the most spatial
and textual relevance to query point within given range.
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EECS 214395--‐Data Structures and Data Mana.docxjack60216
EECS 214/395--‐Data Structures and Data Management
Fall 2014
Programming Assignment No.2—PR Quadtrees
Due: Nov. 11, 2014 midnight
Background
For this project you will build a simple Geographic Information System for storing point data. The
focus is organizing city records into a database for fast search. For each city you will store the name and its
location (X and Y coordinates). Searches can be by either name or location. Thus, your project will
actually implement two trees: you will implement a Binary Search Tree to support searches by name,
and you will implement a variation on the PR Quadtree to support searches by position.
Consider what happens if you store the city records using a linked list. The cost of key operations (insert,
remove, search) using this representation would be Θ(n) where n is the number of cities. For a few
records this is acceptable, but for a large number of records this becomes too slow. Some other
representation is needed that makes these operations much faster. In this project, you will implement a
variation on one such representation, known as a PR Quadtree. A binary search tree gives O(log n)
performance for insert, remove, and search operations (if you ignore the possibility that it is unbalanced).
This would allow you to insert and remove cities, and locate them by name. However, the BST does not help
when doing a search by coordinate. In particular, the primary search operation that we are concerned with in
this project is a form of range query called “regionsearch.” In a regionsearch, we want to find all cities that are
within a certain distance of a query point.
You could combine the (x, y) coordinates into a single key and store cities using this key in a second BST. That
would allow search by coordinate, but does nothing to help regionsearch. The problem is that the BST
only works well for one--‐dimensional keys, while a coordinate is a two--‐dimensional key. To solve these
problems, you will implement a variant of the PR Quadtree. The PR Quadtree i ...
Spot db consistency checking and optimization in spatial databasePratik Udapure
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read and write. In this study, we present the experience we had with these different database management
systems including setup and configuration complexity
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Study of the Class and Structural Changes Caused By Incorporating the Target ...ijceronline
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The document summarizes a proposed system for currency recognition on mobile phones. The system has the following modules: 1) segmentation to isolate the currency from background noise, 2) feature extraction and building a visual vocabulary, 3) instance retrieval using inverted indexing and spatial reranking, 4) classification by vote counting spatially consistent features. The system was adapted for mobile by reducing complexity, such as using an inverted index, while maintaining accuracy. Performance is evaluated using metrics like accuracy and precision.
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Ranking Preferences to Data by Using R-Trees
1. IOSR Journal of Computer Engineering (IOSRJCE)
ISSN: 2278-0661, ISBN: 2278-8727Volume 6, Issue 5 (Nov. - Dec. 2012), PP 30-35
www.iosrjournals.org
www.iosrjournals.org 30 | Page
Ranking Preferences to Data by Using R-Trees
Nageswarrao.Vungarala1
, Manoj Kiran.Somidi2
, Krishnaiah.R.V.3
1
Department of CS, DRK College of Engineering& Technology, Ranga Reddy, Andhra Pradesh, India
2
Department of SE, DRK Institute of Science & Technology, Ranga Reddy, Andhra Pradesh, India
3
Principal Department of CSE, DRK Institute of Science & Technology, Ranga Reddy, Andhra Pradesh, India
Abstract: A Spatial data preference query ranks the given objects based on the quality and their features in
nearest neighborhood. Here “feature” refers to a class of objects in a spatial map such as specific facilities or
services. For example, a real estate agency maintains database which holds the details of flats for rent. Here, A
customer may want to rank the contents with respect to the appropriateness of their locations, the ranks are
given by using the top-k spatial aggregate functions with respect to quality of features and nearest
neighborhood. A neighborhood concept can be specified by the user via different functions. Every customer
maximum prefers the quality of the flat and more facilities in that flat. The potential customer wishes to view the
top 10 flats with the largest size and lowest prices, which is nearest to Hospital, School, Bus top, Market etc. In
this paper, we define top-k spatial preference queries and branch and bound algorithm.
Keywords: Spatial data, Top-k spatial.
I. Introduction
A Spatial databases store data that is related to the objects in space. There are two basic ways for
ranking the objects: 1). Spatial ranking, which orders the objects according to their distance from a reference
point and 2) Non spatial ranking, which orders the objects by an aggregate function on their spatial values. Top-
k spatial algorithm combines these two types spatial and non spatial ranking. A Spatial database systems
contains large collection of geographic entities, which apart from spatial attributes contain non spatial data. A
customer want to rank the contents of this database with respect to the quality of their locations, quantified by
aggregating non spatial characteristics of other features (e.g. Hospital, School, Market etc.) in the spatial
neighborhood of flat. Quality may be subjective and query parametric. A neighborhood concept can be specified
by the user via different functions.
A set of D best data objects based on the quality of feature objects in their spatial neighborhood. Fig 1
illustrates three locations (p1, p2, p3) of user interest and two feature sets (v and t) these are within the spatial
neighborhood of the location. The feature values are labeled with the quality values. The quality values are
normalized to values in [0, 1].
(a) Range score, (b) Influence score
Fig. 1. Top-k spatial preference queries.
According to above example, a customer wants to retrieve the top-k flats from a spatial database which
is maintained by the real estate agency. In this case, the score of each is depends on sum of two qualities those
are size and price. The customer wants to retrieve the top-k flats which are near to the high quality hospital and
high quality restaurant. Fig. 1(a) illustrates the locations of the object data set D (hotel) in white and two other
objects (restaurants) in gray and (cafes) in black. The score p1 of a hotel p is defined in terms of: 1) the
maximum quality for each feature in the neighborhood region of p, and 2) the aggregation of those qualities.
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The spatial neighborhood can be specified by the user to restrict the distance of the eligible feature objects. If
the user wants to rank the flats based on the range score which is nearest neighborhood region to a circular at p
with radius € and used the aggregate functions SUM, MIN and MAX. SUM function is implemented on Fig. 1
the maximum quality of gray and black points within the circle of p1 are 0.9 and 0.6 respectively. Then the
score of P1 is p1= 0.9 + 0.6 = 1.5. Similarly, the maximum quality of gray and black points within the circle of
P2 are 1.0 and 0.1, so the score of P2 is p2 = 1.0 + 0.1 = 1.1 and p3 = 0.7 + 0.7 = 1.4. Hence, within above 3
results the p1 (hotel) has the maximum value and has quality. The SUM function attempts to balance the overall
qualities of all features.
We are implemented the MIN function on Fig. 1, then the top results becomes P3, with the score P3 =
Min {0.7, 0.7} = 0.7, it shows that the top result has reasonable high qualities in all features. The MAX function
is used optimize the quality in a particular feature. For the MAX function the top result is P2, with P2 = Max
{1.0, 0.1} = 1.0 Top-k spatial preference queries comprise a useful tool for a wide range of location based
applications. But processing this query is quite complex. Because it may require computing the scores of all data
objects and select the top-k spatial preference query. It is not always possible to use multidimensional indexes
for top-k spatial retrieval. First such indexes break down in high dimensional spaces [10]. Second top-k spatial
preference queries may involve an arbitrary set of user specified attributes (e.g., size and price) from possible
ones (e.g., size, price, floor etc,). Third, information for ranking s to be combined could appear in different
databases and unified indexes may not exist for them top-k spatial preference queries [2], [8] focusing on the
different sources.
Specifically, we contribute the branch and bound (BB) algorithm and Feature join (FJ) algorithm for efficiently
processing the top-k spatial preference query.
II. Background Knowledge
Object ranking (ordering) is a popular retrieval task in various applications. The tuples in relational
databases are ordered using aggregate functions on their attribute values [2]. Consider a real estate agency
database maintaining information regarding flats available for rent. A customer wishes to view top ten flats with
the largest sizes and lowest prices. Here, the score of each flat is expressed by the sum of two attributes: size and
price. Ranking (ordering) in spatial databases is often associated to the nearest neighbor (NN) retrieval. The NN
returns set of nearest objects to a given query location that satisfies a condition (e.g., restaurants). The set of
interesting object is indexed by an R-tree [3]. Then the index can be traversed in a branch and bound fashion to
obtain the answer [4]. It is not always possible to use multidimensional indexes for top-k spatial preference
retrieval. So such indexes break down in high dimensional spaces [5], [6]. Solutions for top-k spatial preference
queries [2], [7], [8], [9] focus on the efficient merging of object rankings that may arrive from different
sources.
2.1 Spatial Query Evaluation on R-Trees
Several approaches have been proposed for ranking spatial data. In order to handle spatial data
efficiently, an effective indexing mechanism is required. One of the most popular spatial access method is the R-
Tree [3] which indexes minimum bounding rectangles (MBRs) of spatial objects.
Fig. 2 Spatial queries on R-Trees
Fig 2 shows a set of interesting points D= {P1, P2…P8) indexed using an R-tree. A spatial range query
returns the objects in D that intersect the given query region W. For example, consider a range query that asks
for all objects within the shaded area in Fig 2. Starting from the root of the tree, the query is processed by
recursively following entries having MBRs that intersect the query region. For instance, e1 does not match
intersect the query region. The sub tree pointed by e1 cannot contain any query result. In contrast, e2 is followed
by the algorithm and the points in the corresponding node are examined recursively to find the query result p7.
A variant of an R-tree is a R-tree [14]. Here each non-leaf node entry develops an aggregate [10] value (MAX)
for some attribute measures in its sub tree. For instance, a MAX an R-tree can be constructed over the point set
given in Fig. 2, if the entries e1, e2, e3 contain the maximum measure values of sets {P2, P3}, {P1, P8, P7}, {P4,
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P5, P6}, respectively. Assume that the measure values of P4, P5, P6 are 0.2, 0.1, 0.4, respectively. In this case, the
aggregate measure augmented in e3 would be max {0.2, 0.1, 0.4} = 0:4. In this paper, we employ MAX a R-
trees for indexing the feature data sets (e.g., restaurants), in order to accelerate the processing of top-k spatial
Preference queries.
Given a feature data set F and a multidimensional region R, the range top-k query selects the tuples
(from F) within the region R and returns only those with the k highest qualities. Hong et al. [11] indexed the
data set by a MAX a R-tree and developed an efficient tree traversal algorithm to answer the query. Instead of
finding the best k qualities from F in a specified region, range score considers multiple spatial regions based on
the points from the objects data set D, and attempts to find the best k regions.
2.2 Feature based spatial queries
Xia et al. [12] solved the problem of finding top-k sites (e.g., restaurants) based on their influence on
feature points (e.g., residential buildings). As an example, Fig. 3a shows a set of sites (white points), a set of
features (black points with weights), such that each line links a feature point to its nearest site. The influence of
a site pi is defined by the sum of weights of feature points having pi as their closest site. For instance, the score
of p1 is 0.9 + 0.5 = 1.4. Similarly, the scores of p2 and p3 are 1.5 and 1.2, respectively. Hence, p2 is returned as
the top-1 influential site.
(a) Top-k influential (b) Max- influential (c) Min- distance.
Fig. 3 Influential sites and optimal location queries.
Related to top-k influential sites query are the optimal location queries studied in [13], [14]. The goal is
to find the location in space that minimizes an objective function. In Figs. 3(b) and 3(c) features points and
existing sites are shown as black and gray points, respectively. Assume that all feature points have the same
quality. The maximum influence optimal location query [13] finds the location with the maximum influence (as
defined in [12]) where as the minimum distance optimal location query [14] searches for the location that
minimizes the average distance from each feature point to its nearest site. The techniques proposed in [12], [13],
[14] are specific to the particular query types described above and cannot be extended for our top-k spatial
preference queries. The novel spatial queries and joins [15], [16], [17], [18] have been proposed for various
spatial decision.
III. Preliminaries
Let Fc be a feature data set, in which each feature object s € Fc is associated with a quality w(s) and a
spatial point. We assume that the domain of w(s) is the interval [0, 1]. As an example, the quality w(s) of a
restaurant s can be obtained from a ratings provider.
Let D be an object data set, where each object p € D is a spatial point. In other words, D is the set of
interesting points (e.g., hotel locations) considered by the user.
Given an object data set D and m feature data sets F1, F2 . . . Fm, the top-k spatial preference query
retrieves the k points in D with the highest score. Here, the score of an object point p € D is defined as
∏Ө
(p) = AGG {∏Ө
(p) ׀c € [1, m] }
Where AGG is an aggregate function and ∏Ө
(p) is the (Cth)
component score of p with respect to the
neighborhood condition Өand the (Cth)
feature data set Fc. Typical examples of the aggregate function AGG are
SUM, MIN, and MAX.
The component score function, ∏Ө
(p) taken is the range score function. The range score, ∏c
rng
(p) is
taken as the maximum quality w(s) of points s € Fc that are within a given parameter distance € from p, or 0 if
no such point exists.
∏c
rng
(p) = max ({w(s)׀ s €Fc ^ dist (p, s) € €} U {0})
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3.1 Branch and Bound Algorithm
A brute force approach for processing the top-k spatial preference query computes the score of every
point p €D in order to obtain the query results. Brute force approach is expensive as it examines all objects in
D and computes their component scores. The algorithm proposed here can significantly reduce the number of
objects to be examined. The key idea is to compute, for non leaf entries e in the object tree D, an upper bound T
(e) of the score for any point p in the sub tree of e. If T < €, then need not access the sub tree of e, thus
numerous score computations can be saved.
Branch and bound algorithm is a pseudo code of the proposed algorithm based on this idea. BB is
called with N being the root node of D. If N is a non leaf node. Lines 3-5 compute the scores T (e) for non leaf
entries e concurrently. T (e) is an upper bound score for any point in the sub tree of e, with the component
scores TC (e) known so far, derive T+ (e), an upper bound of T (e). If T+ (e) € €, then sub tree of an e cannot
contain better results than those in Wk and it is removed from the set V. In order to obtain points with high
scores early, sort the entries in descending order of T (e) before invoking the above procedure recursively on the
child nodes pointed by the entries in V. If N is a leaf node, compute the scores for all points of N concurrently
and then update the set Wk of the top-k results. Since both Wk and €are global variables, their values are
updated during recursive call of branch and bound algorithm.
The following shows the branch and bound algorithm implementation.
Wk: = new min-heap of size k (initially empty);
€: =0; Δ kth score in Wk
Algorithm BB (Node N)
1: V: = {e׀e €N};
2: If N is non leaf then
3: for c: = 1 to m do
4: compute Tc (e) for all e € V concurrently;
5: remove entries e in V such that T + (e) € €;
6: sort entries e €V in descending order of T (e);
7: for each entry e €V such that T (e) > γ do
8: read the child node Ń pointed by e;
9: BB (Ń);
10: else
11: for c: = 1 to m do
12: compute Tc (e) for all e €V concurrently;
13: remove entries e in V such that T + (e) ≤ €;
14: update Wk (and €) by entries in V;
Upper bound scores Tc (e) of non leaf entries (within the same node N) can be computed concurrently
(at line 4). Upper bound score is to be computed such that 1) the bounds are computed with low I / O cost , and
2) the bounds are reasonably tight, in order to facilitate effective prunning. To achieve this, only level-1 entries
(i.e., lowest level non leaf entries) in Fc are utilized for deriving upper bound scores because: 1) there is much
fewer level-1 entries than leaf entries, 2) high-level entries in Fc cannot provide tight bounds.
3. 2 Feature Join Algorithm
Feature join algorithm is an alternative method for evaluating a top-k spatial preference query to
perform a multyway spatial join [23] on feature trees F1, F2, Fm to obatin combinations of featurepoints which
can be in the neighborhood of some object from D. We first introduce the concept of a combination to be pruned
and finally elaborated the algorithm used to progressively identify the combinations that correspond to query
results.
The following algorithm shows the feature join algorithm,
Wk: = new min-heap of size k (initially empty);
€:= 0; Δ kth
score in Wk
Algorithm FJ (Tree D, Trees F1, F2 . . ., Fm)
1: H: = new max-heap (combination score as the key);
2: insert (F1.root, F2.root. . . Fm. Root) into H;
3: while H is not empty do
4: deheap (f1, f2. . . Fm) from H;
5: if for all c 1€ [1, m], c fc points to a leaf node
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6: for c: = 1 to m do
7: read the child node Lc pointed by fc;
8: Find_ Result (D: root, L1,. . . , Lm);
9: else
10: fc : = highest level entry among f1, f2; . . . ; fm;
11: read the child node Nc pointed by fc;
12: for each entry ec € Nc do
13: insert (f1, f2, . . . ,ec . . . ,fm) into H if its score is Greater than €and it qualifies the query;
Algorithm Find_ Result (Node N, Nodes L1,... Lm)
1: for each entry e € N do
2: if N is non leaf then
3: compute T (e) by entries in L1,.. ., Lm;
4: if T (e) > €then
5: read the child node N’ pointed by e;
6: Find_ Result (N’, L1, . . ., Lm);
7: else
8: compute T (e) by entries in L1,...,Lm;
9: update Wk (and €) by e (when necessary);
IV. Conclusion
Top-k spatial preference queries, provides a novel type of ranking for spatial objects based on qualities
of features in their neighborhood. But processing these queries is quite complex. The brute force approach
computes the scores of every data object by querying on feature data sets. This method is expensive for large
input data sets. The alternative technique, branch and bound algorithm, the algorithm BB derives upper bound
scores for non leaf entries in the object tree and prunes those that cannot lead to better results. The Feature join
algorithm performs a multi way join on feature trees to obtain qualified combinations of feature points and then
search for relevant objects in object tree. Feature join is the best algorithm in cases where the number m of
feature data sets is low and each feature data set is small.
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About Authors
Nageswarrao Vungarala is a student of DRK College of Engineering and Technology, Ranga
Reddy, Andhra Pradesh, India. He has received M.C.A degree and M.Tech Degree in Computer
Science. His main research interest includes Data Mining and Cloud Computing
Manoj Kiran Somidi is a student of DRK Institute of Science & Technology, Ranga Reddy, and
Andhra Pradesh, India. He has received B.Tech Degree in Computer Science and Engineering and
M.Tech Degree in Software Engineering. His main research interest includes Software
Engineering, Data Mining.
Dr.R.V.Krishnaiah is working as Principal at DRK INSTITUTE OF SCINCE &
TECHNOLOGY, Hyderabad, and Andhra Pradesh, INDIA. He has received M.Tech Degree
(EIE&CSE) and Ph.D. His main research interest includes Data Mining, Software Engineering