The document discusses query optimization using case-based reasoning in ubiquitous environments. It proposes adapting case-based reasoning to provide optimal execution plans for new queries using knowledge acquired from past experiences. A case represents an execution plan consisting of the query, problem description, relevant past case, and reasoning process. The solution adapts the relevant past case to optimize the new query. Operations are grouped into families based on having the same condition applied to the same attributes. This allows experiences to be reused to optimize similar new queries.
Column store decision tree classification of unseen attribute setijma
A decision tree can be used for clustering of frequently used attributes to improve tuple reconstruction time
in column-stores databases. Due to ad-hoc nature of queries, strongly correlative attributes are grouped
together using a decision tree to share a common minimum support probability distribution. At the same
time in order to predict the cluster for unseen attribute set, the decision tree may work as a classifier. In
this paper we propose classification and clustering of unseen attribute set using decision tree to improve
tuple reconstruction time.
WEIGHTED CONSTRAINT SATISFACTION AND GENETIC ALGORITHM TO SOLVE THE VIEW SELE...ijdms
The document summarizes a study that models the view selection problem in data warehousing as a weighted constraint satisfaction problem. The researchers encode the view selection problem as a weighted constraint satisfaction problem to find an optimal set of views to materialize. They use the multiple view processing plan framework as the search space and propose a genetic algorithm to select the views to materialize. The experimental results show that the proposed algorithm can effectively select appropriate views for materialization.
Decision tree clustering a columnstores tuple reconstructioncsandit
Column-Stores has gained market share due to promi
sing physical storage alternative for
analytical queries. However, for multi-attribute qu
eries column-stores pays performance
penalties due to on-the-fly tuple reconstruction. T
his paper presents an adaptive approach for
reducing tuple reconstruction time. Proposed approa
ch exploits decision tree algorithm to
cluster attributes for each projection and also eli
minates frequent database scanning.
Experimentations with TPC-H data shows the effectiv
eness of proposed approach.
In the present day huge amount of data is generated in every minute and transferred frequently. Although
the data is sometimes static but most commonly it is dynamic and transactional. New data that is being
generated is getting constantly added to the old/existing data. To discover the knowledge from this
incremental data, one approach is to run the algorithm repeatedly for the modified data sets which is time
consuming. Again to analyze the datasets properly, construction of efficient classifier model is necessary.
The objective of developing such a classifier is to classify unlabeled dataset into appropriate classes. The
paper proposes a dimension reduction algorithm that can be applied in dynamic environment for
generation of reduced attribute set as dynamic reduct, and an optimization algorithm which uses the
reduct and build up the corresponding classification system. The method analyzes the new dataset, when it
becomes available, and modifies the reduct accordingly to fit the entire dataset and from the entire data
set, interesting optimal classification rule sets are generated. The concepts of discernibility relation,
attribute dependency and attribute significance of Rough Set Theory are integrated for the generation of
dynamic reduct set, and optimal classification rules are selected using PSO method, which not only
reduces the complexity but also helps to achieve higher accuracy of the decision system. The proposed
method has been applied on some benchmark dataset collected from the UCI repository and dynamic
reduct is computed, and from the reduct optimal classification rules are also generated. Experimental
result shows the efficiency of the proposed method.
Exploratory data analysis using xgboost package in RSatoshi Kato
Explain HOW-TO procedure exploratory data analysis using xgboost (EDAXGB), such as feature importance, sensitivity analysis, feature contribution and feature interaction. It is just based on using built-in predict() function in R package.
All of the sample codes are available at: https://github.com/katokohaku/EDAxgboost
This document proposes a simple procedure for beginners to obtain reasonable results when using support vector machines (SVMs) for classification tasks. The procedure involves preprocessing data through scaling, using a radial basis function kernel, selecting model parameters through cross-validation grid search, and training the full model on the preprocessed data. The document provides examples applying this procedure to real-world datasets, demonstrating improved accuracy over approaches without careful preprocessing and parameter selection.
A PSO-Based Subtractive Data Clustering AlgorithmIJORCS
There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Recent studies have shown that partitional clustering algorithms such as the k-means algorithm are the most popular algorithms for clustering large datasets. The major problem with partitional clustering algorithms is that they are sensitive to the selection of the initial partitions and are prone to premature converge to local optima. Subtractive clustering is a fast, one-pass algorithm for estimating the number of clusters and cluster centers for any given set of data. The cluster estimates can be used to initialize iterative optimization-based clustering methods and model identification methods. In this paper, we present a hybrid Particle Swarm Optimization, Subtractive + (PSO) clustering algorithm that performs fast clustering. For comparison purpose, we applied the Subtractive + (PSO) clustering algorithm, PSO, and the Subtractive clustering algorithms on three different datasets. The results illustrate that the Subtractive + (PSO) clustering algorithm can generate the most compact clustering results as compared to other algorithms.
A NEW PERSPECTIVE OF PARAMODULATION COMPLEXITY BY SOLVING 100 SLIDING BLOCK P...ijaia
This paper gives complete guidelines for authors submitting papers for the AIRCC Journals. A sliding puzzle is a combination puzzle where a player slides pieces along specific routes on a board to reach a certain end configuration. In this paper, we propose a novel measurement of the complexity of 100 sliding puzzles with paramodulation, which is an inference method of automated reasoning. It turned out that by counting the number of clauses yielded with paramodulation, we can evaluate the difficulty of each puzzle. In the experiment, we have generated 100 * 8 puzzles that passed the solvability checking by countering inversions. By doing this, we can distinguish the complexity of 8 puzzles with the number generated with paramodulation. For example, board [2,3,6,1,7,8,5,4, hole] is the easiest with score 3008 and board [6,5,8,7,4,3,2,1, hole] is the most difficult with score 48653.Besides, we have succeeded in obverse several layers of complexity (the number of clauses generated) in 100 puzzles. We can conclude that the proposed method can provide a new perspective of paramodulation complexity concerning sliding block puzzles.
Column store decision tree classification of unseen attribute setijma
A decision tree can be used for clustering of frequently used attributes to improve tuple reconstruction time
in column-stores databases. Due to ad-hoc nature of queries, strongly correlative attributes are grouped
together using a decision tree to share a common minimum support probability distribution. At the same
time in order to predict the cluster for unseen attribute set, the decision tree may work as a classifier. In
this paper we propose classification and clustering of unseen attribute set using decision tree to improve
tuple reconstruction time.
WEIGHTED CONSTRAINT SATISFACTION AND GENETIC ALGORITHM TO SOLVE THE VIEW SELE...ijdms
The document summarizes a study that models the view selection problem in data warehousing as a weighted constraint satisfaction problem. The researchers encode the view selection problem as a weighted constraint satisfaction problem to find an optimal set of views to materialize. They use the multiple view processing plan framework as the search space and propose a genetic algorithm to select the views to materialize. The experimental results show that the proposed algorithm can effectively select appropriate views for materialization.
Decision tree clustering a columnstores tuple reconstructioncsandit
Column-Stores has gained market share due to promi
sing physical storage alternative for
analytical queries. However, for multi-attribute qu
eries column-stores pays performance
penalties due to on-the-fly tuple reconstruction. T
his paper presents an adaptive approach for
reducing tuple reconstruction time. Proposed approa
ch exploits decision tree algorithm to
cluster attributes for each projection and also eli
minates frequent database scanning.
Experimentations with TPC-H data shows the effectiv
eness of proposed approach.
In the present day huge amount of data is generated in every minute and transferred frequently. Although
the data is sometimes static but most commonly it is dynamic and transactional. New data that is being
generated is getting constantly added to the old/existing data. To discover the knowledge from this
incremental data, one approach is to run the algorithm repeatedly for the modified data sets which is time
consuming. Again to analyze the datasets properly, construction of efficient classifier model is necessary.
The objective of developing such a classifier is to classify unlabeled dataset into appropriate classes. The
paper proposes a dimension reduction algorithm that can be applied in dynamic environment for
generation of reduced attribute set as dynamic reduct, and an optimization algorithm which uses the
reduct and build up the corresponding classification system. The method analyzes the new dataset, when it
becomes available, and modifies the reduct accordingly to fit the entire dataset and from the entire data
set, interesting optimal classification rule sets are generated. The concepts of discernibility relation,
attribute dependency and attribute significance of Rough Set Theory are integrated for the generation of
dynamic reduct set, and optimal classification rules are selected using PSO method, which not only
reduces the complexity but also helps to achieve higher accuracy of the decision system. The proposed
method has been applied on some benchmark dataset collected from the UCI repository and dynamic
reduct is computed, and from the reduct optimal classification rules are also generated. Experimental
result shows the efficiency of the proposed method.
Exploratory data analysis using xgboost package in RSatoshi Kato
Explain HOW-TO procedure exploratory data analysis using xgboost (EDAXGB), such as feature importance, sensitivity analysis, feature contribution and feature interaction. It is just based on using built-in predict() function in R package.
All of the sample codes are available at: https://github.com/katokohaku/EDAxgboost
This document proposes a simple procedure for beginners to obtain reasonable results when using support vector machines (SVMs) for classification tasks. The procedure involves preprocessing data through scaling, using a radial basis function kernel, selecting model parameters through cross-validation grid search, and training the full model on the preprocessed data. The document provides examples applying this procedure to real-world datasets, demonstrating improved accuracy over approaches without careful preprocessing and parameter selection.
A PSO-Based Subtractive Data Clustering AlgorithmIJORCS
There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Recent studies have shown that partitional clustering algorithms such as the k-means algorithm are the most popular algorithms for clustering large datasets. The major problem with partitional clustering algorithms is that they are sensitive to the selection of the initial partitions and are prone to premature converge to local optima. Subtractive clustering is a fast, one-pass algorithm for estimating the number of clusters and cluster centers for any given set of data. The cluster estimates can be used to initialize iterative optimization-based clustering methods and model identification methods. In this paper, we present a hybrid Particle Swarm Optimization, Subtractive + (PSO) clustering algorithm that performs fast clustering. For comparison purpose, we applied the Subtractive + (PSO) clustering algorithm, PSO, and the Subtractive clustering algorithms on three different datasets. The results illustrate that the Subtractive + (PSO) clustering algorithm can generate the most compact clustering results as compared to other algorithms.
A NEW PERSPECTIVE OF PARAMODULATION COMPLEXITY BY SOLVING 100 SLIDING BLOCK P...ijaia
This paper gives complete guidelines for authors submitting papers for the AIRCC Journals. A sliding puzzle is a combination puzzle where a player slides pieces along specific routes on a board to reach a certain end configuration. In this paper, we propose a novel measurement of the complexity of 100 sliding puzzles with paramodulation, which is an inference method of automated reasoning. It turned out that by counting the number of clauses yielded with paramodulation, we can evaluate the difficulty of each puzzle. In the experiment, we have generated 100 * 8 puzzles that passed the solvability checking by countering inversions. By doing this, we can distinguish the complexity of 8 puzzles with the number generated with paramodulation. For example, board [2,3,6,1,7,8,5,4, hole] is the easiest with score 3008 and board [6,5,8,7,4,3,2,1, hole] is the most difficult with score 48653.Besides, we have succeeded in obverse several layers of complexity (the number of clauses generated) in 100 puzzles. We can conclude that the proposed method can provide a new perspective of paramodulation complexity concerning sliding block puzzles.
This document discusses using fuzzy set theory and decision trees to predict student performance. It proposes using fuzzy sets to represent numeric student data like test scores and attendance to allow for imprecise values. A decision tree is generated on this fuzzy data set to classify students as passing or failing. The fuzzy decision tree achieves an accuracy of 81.5% compared to 76% for a non-fuzzy decision tree, indicating fuzzy sets improve predictive performance. Location, attendance, and prior academic performance were identified as important factors impacting student results.
Multimodal Biometrics Recognition by Dimensionality Diminution MethodIJERA Editor
Multimodal biometric system utilizes two or more character modalities, e.g., face, ear, and fingerprint,
Signature, plamprint to improve the recognition accuracy of conventional unimodal methods. We propose a new
dimensionality reduction method called Dimension Diminish Projection (DDP) in this paper. DDP can not only
preserve local information by capturing the intra-modal geometry, but also extract between-class relevant
structures for classification effectively. Experimental results show that our proposed method performs better
than other algorithms including PCA, LDA and MFA.
CATEGORY TREES – CLASSIFIERS THAT BRANCH ON CATEGORYijaia
This paper presents a batch classifier that splits a dataset into tree branches depending on the category type. It has been improved from the earlier version and fixed a mistake in the earlier paper. Two important changes have been made. The first is to represent each category with a separate classifier. Each classifier then classifies its own subset of data rows, using batch input values to create the centroid and also represent the category itself. If the classifier contains data from more than one category however, it needs to create new classifiers for the incorrect data. The second change therefore is to allow the classifier to branch to new layers when there is a split in the data, and create new classifiers there for the data rows that are incorrectly classified. Each layer can therefore branch like a tree - not for distinguishing features, but for distinguishing categories. The paper then suggests a further innovation, which is to represent some data columns with fixed value ranges, or bands. When considering features, it is shown that some of the data can be classified directly through fixed value ranges, while the rest must be classified using a classifier technique and the idea allows the paper to discuss a biological analogy with neurons and neuron links. Tests show that the method can successfully classify a diverse set of benchmark datasets to better than the state-of-the-art.
This document discusses genomic meta-analysis and summarization techniques. It introduces MetaQC for quality control, MetaDE for detecting differentially expressed genes through meta-analysis, and MetaPCA for integrative visualization of multiple genomic studies. MetaQC uses quality measures to determine inclusion/exclusion of studies in meta-analysis. MetaDE detects biomarkers statistically significant across studies using Fisher's and adaptive weighting methods. MetaPCA integrates multiple genomic datasets by finding a common principal component space.
This document summarizes an introduction to deep learning with MXNet and R. It discusses MXNet, an open source deep learning framework, and how to use it with R. It then provides an example of using MXNet and R to build a deep learning model to predict heart disease by analyzing MRI images. Specifically, it discusses loading MRI data, architecting a convolutional neural network model, training the model, and evaluating predictions against actual heart volume measurements. The document concludes by discussing additional ways the model could be explored and improved.
USE OF ADAPTIVE COLOURED PETRI NETWORK IN SUPPORT OF DECISIONMAKINGcsandit
This work presents the use of Adaptive Coloured Petri Net (ACPN) in support of decision
making. ACPN is an extension of the Coloured Petri Net (CPN) that allows you to change the
network topology. Usually, experts in a particular field can establish a set of rules for the
proper functioning of a business or even a manufacturing process. On the other hand, it is
possible that the same specialist has difficulty in incorporating this set of rules into a CPN that
describes and follows the operation of the enterprise and, at the same time, adheres to the rules
of good performance. To incorporate the rules of the expert into a CPN, the set of rules from the
IF - THEN format to the extended adaptive decision table format is transformed into a set of
rules that are dynamically incorporated to APN. The contribution of this paper is the use of
ACPN to establish a method that allows the use of proven procedures in one area of knowledge
(decision tables) in another area of knowledge (Petri nets and Workflows), making possible the
adaptation of techniques and paving the way for new kind of analysis.
VARIATIONS IN OUTCOME FOR THE SAME MAP REDUCE TRANSITIVE CLOSURE ALGORITHM IM...ijcsit
This document summarizes research that implemented the same transitive closure algorithm for entity resolution on three different Apache Hadoop distributions: a local HDFS cluster, Cloudera Enterprise, and Talend Big Data Sandbox. The algorithm was run on a synthetic dataset to discover entity clusters. While the local HDFS cluster produced consistent results matching the baseline, the Cloudera and Talend platforms had inconsistent results due to differences in configuration requirements, load balancing, and blocking behavior across nodes. The experiments highlighted scalability issues for entity resolution processes in distributed environments due to inconsistencies introduced by differences in platform implementations.
1) Interval classifiers are machine learning algorithms that originated in artificial intelligence research but are now being applied to database mining. They generate decision trees to classify data into intervals based on attribute values.
2) The author implemented the IC interval classifier algorithm and tested it on small datasets, finding higher classification errors than reported in literature due to small training set sizes. Parameter testing showed accuracy improved with larger training sets and more restrictive interval definitions.
3) While efficiency couldn't be fully tested, results suggest interval classifiers may perform well for database applications if further tuned based on dataset characteristics. More research is still needed on algorithm modifications and dynamic training approaches.
Test case optimization in configuration testing using ripper algorithmeSAT Journals
Abstract
Software systems are highly configurable. Although there are lots of advantages in improving the configuration, it is difficult to test unique errors hiding in configurations. To overcome this problem, combinatorial interaction testing (CIT) is used to selects strength and computes a covering array which includes all configuration option combinations. It poorly identifies the effective configuration space. So the cost required for testing get increased. In this work, techniques includes hierarchical clustering algorithm and ripper algorithm. It gives high strength interaction which it can be missed by CIT approach and it identifies effective configuration space. We evaluated and comparecoverage achieves by CIT and RIPPER classification with hierarchical clustering. Using this approach we validate loop as well as statement based configurations. Our results strongly suggest that Proto-interaction formed by RIPPER classificationwith hierarchical clusteringcan effectively covers sets of configurations than traditional CIT.
Keywords: Configuration options, Hierarchical Clustering, RIPPER Algorithm
This document discusses enabling interoperability between finite element tools using the HDF5 data format. It describes extracting data like element connectivity, node coordinates, and material properties from an Abaqus output database and exporting it to an HDF5 file with metadata. This allows other tools to access and analyze the data in a standardized way. Future work includes fully transferring problem definitions and exporting images to HDF5 to enable greater integration between simulation programs.
This document discusses the history and implementation of regression tree models. It begins by covering early tree models from the 1960s-1980s like CART and GUIDE. It then discusses more modern unified frameworks using modular packages in R like partykit and mob models. The document provides an example using a Bradley-Terry tree to model preferences from paired comparisons. It concludes by discussing potential extensions to deep learning methods.
An Automatic Medical Image Segmentation using Teaching Learning Based Optimiz...idescitation
Nature inspired population based evolutionary algorithms are very popular with
their competitive solutions for a wide variety of applications. Teaching Learning based
Optimization (TLBO) is a very recent population based evolutionary algorithm evolved
on the basis of Teaching Learning process of a class room. TLBO does not require any
algorithmic specific parameters. This paper proposes an automatic grouping of pixels into
different homogeneous regions using the TLBO. The experimental results have
demonstrated the effectiveness of TLBO in image segmentation.
This document is a machine learning class assignment submitted by Trushita Redij to their supervisor Abhishek Kaushik at Dublin Business School. The assignment discusses data preprocessing techniques, decision trees, the Chinese Restaurant algorithm, and building supervised learning models. Specifically, linear regression and KNN classification models are implemented on population data from Ireland to predict total population and classify countries.
This document describes a new multi-objective evolutionary algorithm called MOSCA2. MOSCA2 improves upon an earlier algorithm called MOSCA by using subpopulations instead of clusters, truncation selection instead of random selection, adding a recombination operator, and adding a separate archive to store non-dominated solutions. The algorithm uses subpopulations, truncation selection, and a deleting procedure to maintain diversity without needing density information or niche methods. It also uses a separate archive that stores and periodically updates non-dominated solutions found, deleting some when the archive becomes full. The algorithm is capable of solving both constrained and unconstrained nonlinear multi-objective optimization problems.
This document presents a study evaluating the performance of machine learning algorithms for network intrusion detection systems (NIDS) using benchmark datasets. Specifically, it applies an AdaBoost-based machine learning algorithm to NIDS and tests its detection accuracy on the KDD Cup 99 and NSL-KDD intrusion detection datasets. The experimental results show that the AdaBoost-based NIDS performs better on the NSL-KDD dataset compared to the KDD Cup 99 dataset, achieving a higher detection rate and lower false alarm rate.
The document discusses three approaches to integrating data mining capabilities with database systems: DMQL, MSQL, and OLE DB for DM. All three aim to allow users to define, populate, and query data mining models through SQL-like interfaces. DMQL focuses on specifying rules to discover. MSQL provides an expressive language for generating and querying association rules. OLE DB for DM supports defining models, populating them from training data, and using the models to predict attributes via a prediction join. An ideal solution would support defining models, querying model results, and applying models to new data within the database system.
Review of Existing Methods in K-means Clustering AlgorithmIRJET Journal
This document reviews existing methods for improving the K-means clustering algorithm. K-means is widely used but has limitations such as sensitivity to outliers and initial centroid selection. The document summarizes several proposed approaches, including using MapReduce to select initial centroids and form clusters for large datasets, reducing execution time by cutting off iterations, improving cluster quality by selecting centroids systematically, and using sampling techniques to reduce I/O and network costs. It concludes that improved algorithms address K-means limitations better than the traditional approach.
Using particle swarm optimization to solve test functions problemsriyaniaes
In this paper the benchmarking functions are used to evaluate and check the particle swarm optimization (PSO) algorithm. However, the functions utilized have two dimension but they selected with different difficulty and with different models. In order to prove capability of PSO, it is compared with genetic algorithm (GA). Hence, the two algorithms are compared in terms of objective functions and the standard deviation. Different runs have been taken to get convincing results and the parameters are chosen properly where the Matlab software is used. Where the suggested algorithm can solve different engineering problems with different dimension and outperform the others in term of accuracy and speed of convergence.
Il benessere legato al giardino oltre a risolvere la funzione terapeutica allontanando lo stress, potenziando i pensieri positivi, riducendo il consumo di alcuni farmaci, risvegliando l'attività motoria, riguarda tutti coloro che desiderano riacquistare una dimensione di benessere personale e relazionale.
This document presents a method to minimize the execution time of SuperSQL queries by decomposing them into multiple SQL queries when possible. It describes an algorithm to check if a SuperSQL query can be divided based on the relationships between attributes. If divisible, the query is broken into independent SQL queries that are executed separately and then combined. Experiments show this approach reduces execution time for some queries. Future work includes handling more query types and more testing.
Una rete di contatti e di competenze, ben connessi tra loro, trovano opportunità di business all’interno di un cowo. I luoghi diventano così il potenziale incubatore di progetti pubblici - privati.
This document discusses using fuzzy set theory and decision trees to predict student performance. It proposes using fuzzy sets to represent numeric student data like test scores and attendance to allow for imprecise values. A decision tree is generated on this fuzzy data set to classify students as passing or failing. The fuzzy decision tree achieves an accuracy of 81.5% compared to 76% for a non-fuzzy decision tree, indicating fuzzy sets improve predictive performance. Location, attendance, and prior academic performance were identified as important factors impacting student results.
Multimodal Biometrics Recognition by Dimensionality Diminution MethodIJERA Editor
Multimodal biometric system utilizes two or more character modalities, e.g., face, ear, and fingerprint,
Signature, plamprint to improve the recognition accuracy of conventional unimodal methods. We propose a new
dimensionality reduction method called Dimension Diminish Projection (DDP) in this paper. DDP can not only
preserve local information by capturing the intra-modal geometry, but also extract between-class relevant
structures for classification effectively. Experimental results show that our proposed method performs better
than other algorithms including PCA, LDA and MFA.
CATEGORY TREES – CLASSIFIERS THAT BRANCH ON CATEGORYijaia
This paper presents a batch classifier that splits a dataset into tree branches depending on the category type. It has been improved from the earlier version and fixed a mistake in the earlier paper. Two important changes have been made. The first is to represent each category with a separate classifier. Each classifier then classifies its own subset of data rows, using batch input values to create the centroid and also represent the category itself. If the classifier contains data from more than one category however, it needs to create new classifiers for the incorrect data. The second change therefore is to allow the classifier to branch to new layers when there is a split in the data, and create new classifiers there for the data rows that are incorrectly classified. Each layer can therefore branch like a tree - not for distinguishing features, but for distinguishing categories. The paper then suggests a further innovation, which is to represent some data columns with fixed value ranges, or bands. When considering features, it is shown that some of the data can be classified directly through fixed value ranges, while the rest must be classified using a classifier technique and the idea allows the paper to discuss a biological analogy with neurons and neuron links. Tests show that the method can successfully classify a diverse set of benchmark datasets to better than the state-of-the-art.
This document discusses genomic meta-analysis and summarization techniques. It introduces MetaQC for quality control, MetaDE for detecting differentially expressed genes through meta-analysis, and MetaPCA for integrative visualization of multiple genomic studies. MetaQC uses quality measures to determine inclusion/exclusion of studies in meta-analysis. MetaDE detects biomarkers statistically significant across studies using Fisher's and adaptive weighting methods. MetaPCA integrates multiple genomic datasets by finding a common principal component space.
This document summarizes an introduction to deep learning with MXNet and R. It discusses MXNet, an open source deep learning framework, and how to use it with R. It then provides an example of using MXNet and R to build a deep learning model to predict heart disease by analyzing MRI images. Specifically, it discusses loading MRI data, architecting a convolutional neural network model, training the model, and evaluating predictions against actual heart volume measurements. The document concludes by discussing additional ways the model could be explored and improved.
USE OF ADAPTIVE COLOURED PETRI NETWORK IN SUPPORT OF DECISIONMAKINGcsandit
This work presents the use of Adaptive Coloured Petri Net (ACPN) in support of decision
making. ACPN is an extension of the Coloured Petri Net (CPN) that allows you to change the
network topology. Usually, experts in a particular field can establish a set of rules for the
proper functioning of a business or even a manufacturing process. On the other hand, it is
possible that the same specialist has difficulty in incorporating this set of rules into a CPN that
describes and follows the operation of the enterprise and, at the same time, adheres to the rules
of good performance. To incorporate the rules of the expert into a CPN, the set of rules from the
IF - THEN format to the extended adaptive decision table format is transformed into a set of
rules that are dynamically incorporated to APN. The contribution of this paper is the use of
ACPN to establish a method that allows the use of proven procedures in one area of knowledge
(decision tables) in another area of knowledge (Petri nets and Workflows), making possible the
adaptation of techniques and paving the way for new kind of analysis.
VARIATIONS IN OUTCOME FOR THE SAME MAP REDUCE TRANSITIVE CLOSURE ALGORITHM IM...ijcsit
This document summarizes research that implemented the same transitive closure algorithm for entity resolution on three different Apache Hadoop distributions: a local HDFS cluster, Cloudera Enterprise, and Talend Big Data Sandbox. The algorithm was run on a synthetic dataset to discover entity clusters. While the local HDFS cluster produced consistent results matching the baseline, the Cloudera and Talend platforms had inconsistent results due to differences in configuration requirements, load balancing, and blocking behavior across nodes. The experiments highlighted scalability issues for entity resolution processes in distributed environments due to inconsistencies introduced by differences in platform implementations.
1) Interval classifiers are machine learning algorithms that originated in artificial intelligence research but are now being applied to database mining. They generate decision trees to classify data into intervals based on attribute values.
2) The author implemented the IC interval classifier algorithm and tested it on small datasets, finding higher classification errors than reported in literature due to small training set sizes. Parameter testing showed accuracy improved with larger training sets and more restrictive interval definitions.
3) While efficiency couldn't be fully tested, results suggest interval classifiers may perform well for database applications if further tuned based on dataset characteristics. More research is still needed on algorithm modifications and dynamic training approaches.
Test case optimization in configuration testing using ripper algorithmeSAT Journals
Abstract
Software systems are highly configurable. Although there are lots of advantages in improving the configuration, it is difficult to test unique errors hiding in configurations. To overcome this problem, combinatorial interaction testing (CIT) is used to selects strength and computes a covering array which includes all configuration option combinations. It poorly identifies the effective configuration space. So the cost required for testing get increased. In this work, techniques includes hierarchical clustering algorithm and ripper algorithm. It gives high strength interaction which it can be missed by CIT approach and it identifies effective configuration space. We evaluated and comparecoverage achieves by CIT and RIPPER classification with hierarchical clustering. Using this approach we validate loop as well as statement based configurations. Our results strongly suggest that Proto-interaction formed by RIPPER classificationwith hierarchical clusteringcan effectively covers sets of configurations than traditional CIT.
Keywords: Configuration options, Hierarchical Clustering, RIPPER Algorithm
This document discusses enabling interoperability between finite element tools using the HDF5 data format. It describes extracting data like element connectivity, node coordinates, and material properties from an Abaqus output database and exporting it to an HDF5 file with metadata. This allows other tools to access and analyze the data in a standardized way. Future work includes fully transferring problem definitions and exporting images to HDF5 to enable greater integration between simulation programs.
This document discusses the history and implementation of regression tree models. It begins by covering early tree models from the 1960s-1980s like CART and GUIDE. It then discusses more modern unified frameworks using modular packages in R like partykit and mob models. The document provides an example using a Bradley-Terry tree to model preferences from paired comparisons. It concludes by discussing potential extensions to deep learning methods.
An Automatic Medical Image Segmentation using Teaching Learning Based Optimiz...idescitation
Nature inspired population based evolutionary algorithms are very popular with
their competitive solutions for a wide variety of applications. Teaching Learning based
Optimization (TLBO) is a very recent population based evolutionary algorithm evolved
on the basis of Teaching Learning process of a class room. TLBO does not require any
algorithmic specific parameters. This paper proposes an automatic grouping of pixels into
different homogeneous regions using the TLBO. The experimental results have
demonstrated the effectiveness of TLBO in image segmentation.
This document is a machine learning class assignment submitted by Trushita Redij to their supervisor Abhishek Kaushik at Dublin Business School. The assignment discusses data preprocessing techniques, decision trees, the Chinese Restaurant algorithm, and building supervised learning models. Specifically, linear regression and KNN classification models are implemented on population data from Ireland to predict total population and classify countries.
This document describes a new multi-objective evolutionary algorithm called MOSCA2. MOSCA2 improves upon an earlier algorithm called MOSCA by using subpopulations instead of clusters, truncation selection instead of random selection, adding a recombination operator, and adding a separate archive to store non-dominated solutions. The algorithm uses subpopulations, truncation selection, and a deleting procedure to maintain diversity without needing density information or niche methods. It also uses a separate archive that stores and periodically updates non-dominated solutions found, deleting some when the archive becomes full. The algorithm is capable of solving both constrained and unconstrained nonlinear multi-objective optimization problems.
This document presents a study evaluating the performance of machine learning algorithms for network intrusion detection systems (NIDS) using benchmark datasets. Specifically, it applies an AdaBoost-based machine learning algorithm to NIDS and tests its detection accuracy on the KDD Cup 99 and NSL-KDD intrusion detection datasets. The experimental results show that the AdaBoost-based NIDS performs better on the NSL-KDD dataset compared to the KDD Cup 99 dataset, achieving a higher detection rate and lower false alarm rate.
The document discusses three approaches to integrating data mining capabilities with database systems: DMQL, MSQL, and OLE DB for DM. All three aim to allow users to define, populate, and query data mining models through SQL-like interfaces. DMQL focuses on specifying rules to discover. MSQL provides an expressive language for generating and querying association rules. OLE DB for DM supports defining models, populating them from training data, and using the models to predict attributes via a prediction join. An ideal solution would support defining models, querying model results, and applying models to new data within the database system.
Review of Existing Methods in K-means Clustering AlgorithmIRJET Journal
This document reviews existing methods for improving the K-means clustering algorithm. K-means is widely used but has limitations such as sensitivity to outliers and initial centroid selection. The document summarizes several proposed approaches, including using MapReduce to select initial centroids and form clusters for large datasets, reducing execution time by cutting off iterations, improving cluster quality by selecting centroids systematically, and using sampling techniques to reduce I/O and network costs. It concludes that improved algorithms address K-means limitations better than the traditional approach.
Using particle swarm optimization to solve test functions problemsriyaniaes
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Best 20 SEO Techniques To Improve Website Visibility In SERP
20110516_ria_ENC
1. Query Optimization Using
Case-based Reasoning in
Ubiquitous Environments
Lourdes Angelica Martinez-Medina
Christophe Bibineau
Jose Luis Zevhinelli-Martini
2009 Mexican International Conference on Computer Science (ENC '09)
2011/05/16 - Ria Mae Borromeo
2. Introduction
Query Optimization
Rely on cost models that are dependent on metadata (statistics,
cardinality estimates)
Typically restricted to execution time estimation
Problem
There are computational environments where metadata
acquisition and support are expensive.
i.e. Ubiquitous environments
Proposed Solution
Query Optimization technique based on learning, particularly
case-based reasoning
2
3. Ubiquitous Environment
Integrates information from
different computational tools and
application
Characteristics
1. Heterogeneity ( )
• extensive range of computational
resources and electronic devices
• devices have different physical and logical
characteristics
2. Dynamicity ( )
• resources change continuously due to
mobility
• communication network properties and
the resources that interact with it vary
3
4. Ubiquitous Environment
3. Distribution ( )
• resources are distributed within a physical space thus information used by these
resources are also distributed
4. Autonomy ( )
• resources can change their availability status anytime
6. Physical Constraints ( )
• i.e.: processing and storage capability, energy consumption, location
7. Metadata lack ( )
• Constant changes --> Expensive maintenance --> No global schema
4
5. ill be available again. is composed by three phases: logical, global, and physical
s. Resources present physical lim- Logical and physical optimization phases are related to cen
Classical Query Optimization
ain their appropriate operation, e.g.
rage capability, energy consump-
tralized environments. Global optimization is required in
distributed environment. Figure 1 illustrates the optimization
ng others. A device or a process is phases of the typical optimization process.
e a task only if it counts with the
Evaluation cost models used
ational resources. It is convenient
for most of classical query
sk performance based on specific
optimization techniques are
he resource characteristics previ-
tightly tied to metadata
make difficult the acquisition and
use.
tadata like cardinality and statistics
alues. There is not a global schema
Each phase requires
utational environments, its mainte-
nsive different constant changes
due to the metadata types
and has different
ational environments metadata ac-
optimization objectives
ce is very expensive. Ubiquitous
must provide a set of methods to
m available resources. The proper-
ources in ubiquitous environment
s for query processing. Some of
Figure 1. Phases of the optimization process
metadata required for estimating
xecution plans (possible execution
5
esults of a query) as a result of
6. Classical Query Optimization
Logical Optimization
Aims to reduce the number of tuples combined as
intermediate results
Appropriate order for applying selection, projection and join
operators must be decided
Uses heuristics and metadata
Result:
Figure 2. Algebraic query trees
6
7. Classical Query Optimization
Global Optimization
Aims to minimize communication cost related to interactions
among resources and a set of views
Global optimizer: decides where to perform each part of the
execution tree
Result: new execution tree with communication operators
7
8. Classical Query Optimization
Physical Optimization
Aims to reduce disk access for retrieving requested data and
minimize execution time for executing query plans
Metadata related to execution context is required
Figure 2. Algebraic query trees
Algebraic query trees 8
timization Figure 3. Query execution plan
9. Contribution of the Paper
Proposes a query optimization technique for ubiquitous
environments
Allows query optimization according to user requirements
Query optimization based on learning
Goal: Improve or acquire new capabilities rom experience
related some specific tasks
9
10. Query Optimization Based on Learning
Learn from past experience!
Experience : the knowledge gained from a problem resolution
Learning : the acquisition of knowledge in order to improve the
behavior or to acquire new capabilities from previous
experiences
Machine Learning : a sub-discipline of AI that is in-charge of
designing and developing methods that allow computers to
automatically learn in order to improve or create specific
capabilities
10
11. Case-based Reasoning
Proposes a reasoning process that aims to solve new
problems using the experience gained when similar
problems are solved
Case minimum unit of reasoning
Problem Description
Solution
Set of annotations that
describe how the
solution was derived
11
12. consists of (i) a problem description, (ii) its correspondent
solution, and, (iii) a set of annotations that describe how s
Case-based Reasoning Process
the solution was derived. Case based reasoning has been t
formalized as a four-step process: retrieve, reuse, review and
retain [7].
(4) Store as a new (1) Get relevant cases
case in the memory
(2) Adjust the solution
(3) New solution must of the relevant case
be verified in the real to the problem
world (simulation)
Figure 4. Case-based reasoning process
12
13. Case-based Reasoning Adaptation to
Query Optimization
Adapts case-based reasoning to provide optimal execution plans
for new queries
Uses the knowledge acquired from experience to optimize and
execute similar queries
The solution is represented by the current execution plan:
1. Query
2. Problem
3. Case
4. Reasoning Process
13
14. to solve new The whereClause specifies the set of conditions (for data
milar problems
f reasoning. It 1. Query
selection and data combination or join) that must be verified
by the data to form part of the query result.
correspondent Figure 5 illustrates the model that we propose for repre-
describe how
Modular part of knowledge in the definition of and join operations are
senting a query. In a query, selection a problem & case
ning haspiece of knowledge that links amost frequent. the existing
The been the most important and problem with
use, cases and
review
selectClause
fromClause
whereClause
Query Representation (UML Diagram)
Figure 5. Query representation (UML diagram)
ss
14
15. 1. Query
Query Operation
Type
Select condition(atttexp, cnstexp)
Join condition(attrexp.a, attrexp.b)
Set of attributes
Specific Condition
Q = {O1, O2, O3, O4 }
SELECT Rest.nom
FROM Resto, Ville, Region
WHERE Region.nom = ‘RA’ O1
AND Resto.spec = ‘IT’ O2
AND Resto.vil = Ville.nom O3
AND Ville.numDep = Region.numDep O4
15
16. We propose the concept of operation family in ord
1. Query
group operations that include the same condition applie
the same attributes and for this reason, the same relat
Two operations ox and oy pertain to the same oper
Operation Family
family if they associated to asame operation families or join)
All queries are are of the set of type (selection
Used to group operations that include the same condition
involve thethe sameattributesand sameof them must pertain
applied to same attributes (each relations
theTwo operations Ox and Oy respectively). An operation fami
same data source are from the same operation family if:
represented as follows:
same operation type (selection or join)
same attributes
(1) R.an = {on | on = condition(R.an ,value)}
an attribute that pertains
The operation set
operations family is composed
by
R.an the relation R
to
operations set on with a condition of the
condition(R.an , value), where an is an attribute
16
17. of all possible comparison operators: Equal, EqualOrLower,
set. These operations are members of different operation the T p
Lower, GreaterOrEqual, Greater and Different. All the
families: R1.a1 , R2.a2 and R1.a3,R2.a4 . Equation (2) inclu
1. Query
queries are associated to a set of operation families. The
shows the operationa familiesQ is that are associated to each
Q defined by an operations
with
unde
whereClause of query simi
requi
operations in Q.
set. These operations are members of different operation solv
The whereClause ,of a query Q is defined by. an operations set Th
families: R1.a1 R2.a2 and R1.a3,R2.a4 Equation (2) within
of
(On) Q the {
shows = operation families Q that are ,associated to }
(2) R1.a1 , R2.a2 , R1.a3,R2.a4 R2.a4,R3.a5 each com
simil
These operations are members of different operation families
operations in Q. that
solve
Operation families associated to each operation in Q
Each different combination of operation families R.an of int exec
conforms a = { R1.a1 , R2.a2 , i.e. the class R2.a4,R3.a5 } by comp
(2) Q class description, R1.a3,R2.a4 , Cn defined chan
Class operation families in (3). The queries are classified in a
the Description (Cn) that2a
set ofEach different combinationoperation families mustR.an
Each different combination of of operation families
classes. execu
conform text
to conforms a class description, i.e. the class Cn defined by
this. chang
the operation families in (3). The queries are classified in a Figu
(3) Cn = { Rn.an , Rm.am , Rn.ap,Rm.aq , R2.a4,R3.a5 } 2)
set of classes. text e
composed of all queries that contain at least one operation
Figur
that (3)class=Cto is composed specified families that contain
The Cn {n Rn.an ,ofRm.amby Rn.ap,Rm.aq ,Qn
pertains each the , all queries
R2.a4,R3.a5 }
at least one operation that pertains to each of the specified
families as definedisin (4). Thisby all queries Qn Qn pertains
The class Cn composed means, a query that contain
17
18. The class Cn is composed by all queries Qn that contain at least one op
at least one operation that pertains to each of the specified families as defi
1. Query
families as defined in (4). This means, a query Qn pertains to the class Cn
selection C if and only ifpertains operation family family
to the class n operation o2 for all to operation that describes C
that Qn, pertains,to operationCnoif andnonlyQnto operation family is of
R2.a2 the Cn exists class
describes join the an operation o in if for all operation family
3 pertains such as this operation
operation is of the, form nofthe operation family n o4 Cn such pertains
F that describes C , exists an operation O in that as this
R2.a4,R1.a3 and the join operation .
to the operation the form of the operation family F (4) Qand Cn i
operation is of family R1.a1,R3.a6 . The operator n ∈
(4) Qnattribute (∀ Rn.an ∈ not) ∃ ((on ∈ Qn ) ∧ determine the
the ∈ Cn iff value are Cn important to (on ∈ Rn.an ))
Rn.an ))
operation family to which a specific operationVille
Relation R1 pertains,
Q = {O1, O2, O3, O4 }
the important knowledge is related to a1 the operation to
According
numReg
According to the query Q presented above, the selection operation o1 Fi
type and the attribute(s) included in the a2
SELECT Rest.nom operation. The
spec p
FROM o1 pertains to operation family
operationResto, Ville, Region R3.a5 , the nom
a3
operation families ‘RA’
WHERE Region.nom = described before make a4
O1
up a class a).
vil
Any Resto.spec = ‘IT’composed by operations that pertain
AND query that is O2
AND Resto.vil = Ville.nom Relation R2 Resto
toAND Ville.numDep = Region.numDep pertains to the same !!! b).
the families described before O3
a5
class
nom
O4
a6 num
a) C = { R3.a5 , R2.a2 , R2.a4,R1.a3 and R1.a1,R3.a6
b) q ∈ C iff (∀ Rn.an ∈ Cn )∃((on ∈q)∧(on ∈ Rn.an ))
18
19. computational resources consumed by the query and those
that are available at the moment that the new query will be
n
y
2. Problem
executed as well as in the optimization objective that can
changes each time the query is executed.
a 2) Problem: A problem is composed by a query, a con-
text execution representation, and an optimization objective.
Specifies an optimized query, optimization parameters and
measures illustrates to computational resources available of query
Figure 6 related the components of a problem.
execution
context
n query
d optimization
ns target
is
∈
Problem Representation (UML Diagram)
n Figure 6. Problem representation (UML diagram)
e 19
20. available memory, and remaining energy, among others.
Finally, the optimization objective indicates the resource or
2. Problem
set of resources that will be optimized, e.g. minimize energy
consumption. Figure 7 shows an example.
Figure 9
Context - representsFigure 7. An example ofcomputational
measure of the a problem resources
instance sol
available when the query is executed which is a
The set of touples that represent the instance of context de- projection,
Optimization Objectiveis: indicates{ the resource or set of data source
picted on Figure 7 - Context = <memory, 400>, <CPU,
resources75>, <energy, 70> } . Finally, the optimization objective
that will be optimized consumed
indicates the resource or resources from which their con- posed quer
sumption must be optimized.20 Typically, optimization means { <memory,
21. minimize the utilization of these resources. According to o
example, the optimization objective is minimize the memo
3. Case
consumption specified by F(memory).
3) Case: A case is composed of a query, a solution (que
plan) and a set of evaluation measures used to express t
Specifies an optimized query, the solution query. Figure query and t
optimization objective of a to solve the 8 illustrates
the measures related to computational resources that were
components of a case.
consumed by the query execution
query
solution
evaluation measures used to
express optimization objective
Case Representation (UML Diagram)
Figure 8. Case representation (UML diagram)
21
22. imization target to a set of measures collected during the query execution.
cribed as a set These measures are represented as couples of the form
that represents
ilable when the 3. Case
<attribute, value> and express the computational resources
(e.g. memory, CPU, or energy) consumed by the query
de CPU charge, execution. Figure 9 shows an example.
among others.
the resource or
minimize energy
Query - optimization target that hasof a case evaluated and solved
Figure 9. An example been
m Solution - physical execution plan that of this model. Such
Figure 9 presents a simple instance solves the query
instance solves the query Q by means of the query problem
which is an ordered and pertinent sequence of selection,
Evaluationprojection, sort, and join collected during query of
ce of context de- - set of measures operations for accessing a set execution
, 400>, <CPU, data sources. The set of touples representing the resources
22
zation objective consumed during the query evaluation applying the pro-
23. are solved. A case is the minimum unit of reasoning. It by t
consists of (i) a problem description, (ii) its correspondent F
solution, and, (iii) aReasoning Process
4. set of annotations that describe how sent
the solution was derived. Case based reasoning has been the
formalized as a four-step process: retrieve, Retrieval review and
reuse,
retain query class, query plan
[7].
Retention
* The
* Get relevant cases using a
similarity function
and consumption measures * If there is no relevant case in
are stored in form of a case the case base, a new query plan
within the case base must be psuedo-randomly
Retrieval generated to increase the query
optimizer knowledge
Retention Reuse
Reuse
* Adjust the solution of the
Review relevant case to the
* Execution plan is problem
verified Review * The matching processes
depends on the cases’
23 similarity
Figure 4. Case-based reasoning process
24. relevant case within the class must be retrieved by means
Similarity Function
of an intra-class similarity function [10][11]. When the most
relevant case is retrieved, a detailed comparison between the
clauses of the new query and the relevant query (the query
Inter-class Similarity Function
included by the relevant case) is carried out. This determines
* used to define membership of a query
a similarity level between the two queries.
These functions are based on the contrast model of
similarity proposed by Tversky [12] that allow us to
determine Intra-class Similarity Function
the similarity between two objects by means
* used to retrieve most relevant case
of a feature-matching function. Similarity increases as
most common features and decreases as most distinctive
Uses features [13]. The formalization of the original definition is
a feature-matching function
Similarity increases as most common features and decreases as
expressed as follows [12]:
most distinctive features
(5) S (a, b) = θf(A ∩ B) - αf(A - B) - βf(B - A)
Similarity between a and b, is defined in terms of the
24
25. ion families and as a decreasing function of distinctive families,
go- in other words, families that pertain to one query but not the
ific Inter-class similarity
other. The function can be applied to both classes, each one
ing defined by a set of operation families, or applied to a query
and a class. In this case, it is necessary to determine the
Increasing function of common operation families
mp- operation families related to the involved operations. The
Decreasing function of distinctive families
the formalization of this definition in terms of the similarity
Determine operation and a class is expressedinvolved operations
between a query families related to the as follows:
(6) S(C1 ,Q) = θ (C1 ∩ Q) - α (C1 -Q) - β (Q-C1 )
ase-
vantoperation families commonC C1 and Qis defined in terms of
Similarity between to and Q,
1
her-features that pertain tocommon to C and Q, C ∩ Q, the
operation families C1 only 1 1
features that pertain to Q only
on features that pertain to C1 but no to Q, C1 - Q, and those
em. that pertain to Q but no to C1 , Q - C1 . The function f
ase refers particularly to operation families . According to the
ble purpose of our work, these are the features that must be
the compared.
the 25
For practical purposes, suppose that we know the class
26. ble of the query q and the definition of the classes c1 and c2 .
wo purpose of our work, these are the features that must be
the compared.
tep
the
ost
Inter-class similarity
q For practical3 } c = { R.a1 , ∈ R.a2 ,weR.a3,R.a4 } class
= {o1 , o2 , o purposes, suppose that know the
ans of the{query ,q and the definition }of the classes c1 and c2 .
c1 = R.a1 R.a2 , R.a3,R.a4
wo c2 = { R.a1 , R.a2 , x }
ost
tep
the q = {o1 , o2 , o3 } c = { R.a1 , ∈ R.a2 , R.a3,R.a4 }
most
ery c1From R.a1 ,intersections between the query class c that
= { the R.a2 , R.a3,R.a4 }
ans describesR.a1 , query, q x }
nes c2 = { the R.a2 and the classes c1 and c2 , it is
most possible to state that the query class c is similar to c1 .
the Compute for intersections of C with C1 and C2
of
ery From the intersections between the query class c that
to
nes describes the 1 ∩Q)={ and ,the classes c1 and c2 , it is
S(c1 ,q) = (C query q R.a1 R.a2 , R.a3,R.a4 }
ans possible = state∩Q)={ R.a1 , R.a2 } c is similar to c1 .
S(c2 ,q) to (C 2 that the query class
as
of Query class C is similar to C1
ive
to B. Intra-class 1 ∩Q)={ R.a1 , R.a2 , R.a3,R.a4 }
S(c1 ,q) = (Csimilarity
n is
ans S(c2 ,q) = (C2 ∩Q)={ function aims to find the most similar
Intra-class similarity R.a1 , R.a2 }
as queries with respect to a new query, which is desired to
tive be Intra-class similarity same class. In this step, all the
B. optimized, within the
n is compared queries are defined exactly to find the most similar
Intra-class similarity function26aims by the same operations
(operation type and involved attributes), the is desired to
queries with respect to a new query, which difference is
27. queries with respect to a new query, which is desired to
be optimized, within the same class. In this step, all the
Intra-class Similarity
compared queries are defined exactly by the same operations
(operation type and involved attributes), the difference is
he related to the comparison operators, as well as the attribute
ain Aims to find the most similar two queries Q and to a is
values. Similarity between queries with respect Q2 new query
1
no
All defined as an increasing function ofoperationsoperations
compared queries have the same common
ve
Comparison operators or attribute values may differ and
(identical operations in terms of its type, attributes
of operators). The formalization of this definition is as follows:
de Increasing function of common operations
ies (7) S (Q1 , Q2 ) = θo(Q1 ∩ Q2 ) - αo(Q1 - Q2 ) - βo(Q1 - Q2 )
ity
Operations that are common to Q1 and features that pertain to
Q1 but not to Q2
!!"
Find the query that contains the maximum number of operation
mappings!
27
28. two main modules, the case-reasoner and the execution plan
ons in common, they differ in the operator
generator. The case-base reasoner is in charge of adapting
join operation. Also, q1 and q2 have two
Query Optimizer Architecture
the solutions of similar queries to the new situation. The ex-
mmon, they differ in the operator applied by
ecution plan generator is in charge of generating new query
n. Finally, q1 and q5 have only one operation
plans in a pseudo-aleatory way. The case-base reasoner is
ording to this analysis, q2 is the most similar
the most complex of the two modules but the smartest, on
ect to q1 because contains the maximum
the other hand, the execution plan generator is simpler and
Reutilizes the solutions related to queries that does not been solved
tion mappings. q5 is the most different query
probably faster; however it have apply machine learning
cause it contains the minimum number of
techniques. Figure 10 illustrates the optimizer architecture.
ngs. Generates new has exactly the
On the other hand, q1 solutions
f mappings with q3 and q4 . How can we
hese two queries is the most similar to q1 .
A.
vels Case-based Reasoner
level 1. Smart queries indicates which
between two Search Engine
levant query must be adapted. This adapta-
ormed 2. Adapter and Where clauses.
just over Select
lause, interesting attributes to be projected
3. Execution Manager
he Where clause, comparison operators or
ted to the variablesBase Manager the
4. Case can be modified. On
From clause can not be changed because the
ried can not be changed. Table I illustrates
arity levels. Here, selectClause is expresed
B. Execution Plan Generator
se as FC and whereClause as WC.
n must be performed for the similarity levels
). If the similarity level is (3) the From Figure 10. Optimizer architecture
ry clauses are equal, the adaptation must
28
n the select clause, which means that the
29. Case-based Reasoner
Adapts solutions of similar queries to the new situation
1. Smart Search Engine
• retrieves relevant cases
• applies Inter and Intra-class Similarity functions
• selects the query that minimizes the optimization parameters
2. Adapter
• adapts the query plan included in the relevant case to query
problem specifications
• used to facilitate and minimize the cost of the adaptation
process
29
30. Case-based Reasoner
3. Execution Engine
• tests the new query execution plan created by the adaptation
module
4. Case Base Manager
• allows to retain a new knowledge in form of a case
• similarity function is also used
30
This is basically the summary of the entire paper\n
\n
\n
\n
Result: algebraic query tree that optimizes the order in which operators must be applied. \nTree A is not the best plan because the selection operation is applied before the join.\nTree B is the optimal algebraic plan because all selection and projection operations are applied as soon as possible\n\n
In an ubiquitous environment, there are no global views because it&#x2019;s expensive!\n\n
Given: Algebraic Tree (from logical optimized) \nResult: All corresponding execution plans that specify the implementation of each algebraic operator\n\n
- Classical query optimization techniques typically generate execution plans that are optimized according to a single dimension, query execution time.\n- Useful knowledge must be obtained from previously executed queries and be managed and exploited by means of automatic learning techniques\n- GOAL: improve or acquire new capabilities from experience related to some specific tasks\n- Query evaluation time is no longer the main optimization objective\n
Given a new query Q, an existent query plan is retrieved if it can be adapted to Q. Also, it is required to verify if it is possible to accomplish its execution with the computational resources available at the moment of query execution (mem, CPU, energy)\n
\n
\n
\n
It is also necessary to pay attention on the computational resources consumed by the query and those that are available at the moment that the new query will be executed as well as in the optimization objective that can change each time the query is executed.\n
\n
\n
\n
The operator and the attribute value are not important to determine the operation family to which a specific operation pertains, the important knowledge is related to the operation type and the attribute(s) included in the operations\n
\n
\n
\n
\n
\n
Similarity of a and b is defined in terms of features common to a and b\nminus the features that pertain to a but not to b\nand those that pertain to b but not to a\ntheta, alpha, beta : non-negative valued parameters that determine the relative weight of the three components of similarity\n- provide the flexibility when modifying the importance of similarities or differences accdng to area of application\n