Query Selection Research Articles

A hybrid evolutionary algorithm with guided mutation for minimum weight dominating set

This paper presents a hybrid evolutionary algorithm with guided mutation (EA/G) to solve the minimum weight dominating set problem (MWDS) which is Â?Â?Â?Â?$\mathcal {N}\mathcal {P}$-hard in nature not only for general graphs, but also for unit disk graphs (UDG). MWDS finds practical applications in diverse domains such as clustering in wireless networks, intrusion detection in adhoc networks, multi-document summarization in information retrieval, query selection in web databases etc. EA/G is a recently proposed evolutionary algorithm that tries to overcome the shortcomings of genetic algorithms (GAs) and estimation of distribution algorithms (EDAs) both, and that can be considered as a cross between the two. The solution obtained through EA/G algorithm is further improved through an improvement operator. We have compared the performance of our hybrid evolutionary approach with the state-of-the-art approaches on general graphs as well as on UDG. Computational results show the superiority of our approach in terms of solution quality as well as execution time.

Interactive Query-Based Debugging of ASP Programs

Broad application of answer set programming (ASP) for declarative problem solving requires the development of tools supporting the coding process. Program debugging is one of the crucial activities within this process. Modern ASP debugging approaches allow efficient computation of possible explanations of a fault. However, even for a small program a debugger might return a large number of possible explanations and selection of the correct one must be done manually. In this paper we present an interactive query-based ASP debugging method which extends previous approaches and finds the preferred explanation by means of observations. The system automatically generates a sequence of queries to a programmer asking whether a set of ground atoms must be true in all (cautiously) or some (bravely) answer sets of the program. Since some queries can be more informative than the others, we discuss query selection strategies which - given user's preferences for an explanation - can find the most informative query reducing the overall number of queries required for the identification of a preferred explanation.

Active learning for semi-supervised clustering based on locally linear propagation reconstruction

The success of semi-supervised clustering relies on the effectiveness of side information. To get effective side information, a new active learner learning pairwise constraints known as must-link and cannot-link constraints is proposed in this paper. Three novel techniques are developed for learning effective pairwise constraints. The first technique is used to identify samples less important to cluster structures. This technique makes use of a kernel version of locally linear embedding for manifold learning. Samples neither important to locally linear propagation reconstructions of other samples nor on flat patches in the learned manifold are regarded as unimportant samples. The second is a novel criterion for query selection. This criterion considers not only the importance of a sample to expanding the space coverage of the learned samples but also the expected number of queries needed to learn the sample. To facilitate semi-supervised clustering, the third technique yields inferred must-links for passing information about flat patches in the learned manifold to semi-supervised clustering algorithms. Experimental results have shown that the learned pairwise constraints can capture the underlying cluster structures and proven the feasibility of the proposed approach.

Improved selectivity estimator for XML queries based on structural synopsis

With the increasing popularity of XML database applications, the use of efficient XML query optimizers is becoming very essential. The performance of an XML query optimizer depends heavily on the query selectivity estimators it uses to find the best possible query execution plan. In this work, we propose and evaluate a novel selectivity estimator, based on a structural synopsis, called SynopTech. The main idea of SynopTech is the generation of a summary tree by labeling the nodes of the source XML data tree using a fingerprint function and merging subtrees with similar structures. The generated summary tree is then used by SynopTech to estimate the selectivity of given queries. We experimented the proposed approach with four benchmark datasets of different structural characteristics and using different types of queries. Comparing with the Sampling algorithm, one of the state-of-the-art algorithms for selectivity estimations, SynopTech achieved lower selectivity estimation error rates, yet with very low memory budget. For example, for linear and existential queries, SynopTech had perfect estimations whereas the Sampling algorithm had an error rate of up to 70 %. For regular twig queries, SynopTech had a maximum error rate of 4.12 % whereas the Sampling algorithm had more than 55 %.

Active Learning by Querying Informative and Representative Examples.

Active learning reduces the labeling cost by iteratively selecting the most valuable data to query their labels. It has attracted a lot of interests given the abundance of unlabeled data and the high cost of labeling. Most active learning approaches select either informative or representative unlabeled instances to query their labels, which could significantly limit their performance. Although several active learning algorithms were proposed to combine the two query selection criteria, they are usually ad hoc in finding unlabeled instances that are both informative and representative. We address this limitation by developing a principled approach, termed QUIRE, based on the min-max view of active learning. The proposed approach provides a systematic way for measuring and combining the informativeness and representativeness of an unlabeled instance. Further, by incorporating the correlation among labels, we extend the QUIRE approach to multi-label learning by actively querying instance-label pairs. Extensive experimental results show that the proposed QUIRE approach outperforms several state-of-the-art active learning approaches in both single-label and multi-label learning.

Active constrained fuzzy clustering: A multiple kernels learning approach

In this paper, we address the problem of constrained clustering along with active selection of clustering constraints in a unified framework. To this aim, we extend the improved possibilistic c-Means algorithm (IPCM) with multiple kernels learning setting under supervision of side information. By incorporating multiple kernels, the limitation of improved possibilistic c-means to spherical clusters is addressed by mapping non-linear separable data to appropriate feature space. The proposed method is immune to inefficient kernels or irrelevant features by automatically adjusting the weight of kernels. Moreover, extending IPCM to incorporate constraints, its strong robustness and fast convergence properties are inherited by the proposed method. In order to avoid querying inefficient or redundant clustering constraints, an active query selection heuristic is embedded into the proposed method to query the most informative constraints. Experiments conducted on synthetic and real-world datasets demonstrate the effectiveness of the proposed method.

DGFIndex for smart grid

In Smart Grid applications, as the number of deployed electric smart meters increases, massive amounts of valuable meter data is generated and collected every day. To enable reliable data collection and make business decisions fast, high throughput storage and high-performance analysis of massive meter data become crucial for grid companies. Considering the advantage of high efficiency, fault tolerance, and price-performance of Hadoop and Hive systems, they are frequently deployed as underlying platform for big data processing. However, in real business use cases, these data analysis applications typically involve multidimensional range queries (MDRQ) as well as batch reading and statistics on the meter data. While Hive is high-performance at complex data batch reading and analysis, it lacks efficient indexing techniques for MDRQ. In this paper, we propose DGFIndex, an index structure for Hive that efficiently supports MDRQ for massive meter data. DGFIndex divides the data space into cubes using the grid file technique. Unlike the existing indexes in Hive, which stores all combinations of multiple dimensions, DGFIndex only stores the information of cubes. This leads to smaller index size and faster query processing. Furthermore, with pre-computing user-defined aggregations of each cube, DGFIndex only needs to access the boundary region for aggregation query. Our comprehensive experiments show that DGFIndex can save significant disk space in comparison with the existing indexes in Hive and the query performance with DGFIndex is 2-50 times faster than existing indexes in Hive and HadoopDB for aggregation query, 2-5 times faster than both for non-aggregation query, 2-75 times faster than scanning the whole table in different query selectivity.

A Neural Approach Under Active Learning Mode for Change Detection in Remotely Sensed Images

In this paper, a change detection technique using neural networks in active learning framework is proposed under the scarcity of labeled patterns. In the present investigation, two variants of radial basis function neural networks and a multilayer perceptron are used as learners. Instead of training the network (or ensemble of networks) with randomly collected labeled patterns, in the proposed work, the network (or ensemble of networks) is iteratively trained with label patterns, collected using the query functions. Here, two query selection strategies are used: uncertainty sampling and query-by-committee. In this way, the most informative set of labeled patterns can be iteratively generated by querying. To evaluate the effectiveness of the proposed approach, the experiments are conducted on multi-temporal remotely sensed images. The results obtained using the proposed active learning framework are found to be encouraging.

Patch-based active learning (PTAL) for spectral-spatial classification on hyperspectral data

Active learning (AL) has been shown to be a useful approach to improving the efficiency of the classification process for remote-sensing imagery. Current AL methods are essentially based on pixel-wise classification. In this paper, a new patch-based active learning (PTAL) framework is proposed for spectral-spatial classification on hyperspectral remote-sensing data. The method consists of two major steps. In the initialization stage, the original hyperspectral images are partitioned into overlapping patches. Then, for each patch, the spectral and spatial information as well as the label are extracted. A small set of patches is randomly selected from the data set for annotation, then a patch-based support vector machine (PTSVM) classifier is initially trained with these patches. In the second stage (close-loop stage of query and retraining), the trained PTSVM classifier is combined with one of three query methods, which are margin sampling (MS), entropy query-by-bagging (EQB), and multi-class level uncertainty (MCLU), and is subsequently employed to query the most informative samples from the candidate pool comprising the rest of the patches from the data set. The query selection cycle enables the PTSVM model to select the most informative queries for human annotation. Then, these informative queries are added to the training set. This process runs iteratively until a stopping criterion is met. Finally, the trained PTSVM is employed to patch classification. In order to compare this to pixel-based active learning (PXAL) models, the prediction label of a patch by PTSVM is transformed into a pixel-wise label of a pixel predictor to get the classification maps. Experimental results show better performance of the proposed PTAL methods on classification accuracy and computational time on three different hyperspectral data sets as compared with PXAL methods.

An adaptive questioning procedure for eliciting PROMETHEE II's weight parameters

For most decision making problems, finding representative parameters of a decision maker’s preferences remains a challenging task. We adapt to the PROMETHEE II outranking method an eliciting procedure named Q-Eval that has been developed in the context of multi-attribute utility theory (MAUT). It refines the estimation of the preference parameters by iteratively querying the DM through pairwise action comparisons. After formalising an extension of this method to two other types of queries: 1) selection of one action from a subset; 2) ranking of a subset of actions, we propose an adaptive query selection scheme that presents to the DM the most discriminating query type at each step of the process. Simulation results show that this approach improves the efficiency of the eliciting phase in terms of convergence speed.

An active learning approach with uncertainty, representativeness, and diversity.

Big data from the Internet of Things may create big challenge for data classification. Most active learning approaches select either uncertain or representative unlabeled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for query selection, they are usually ad hoc in finding unlabeled instances that are both informative and representative and fail to take the diversity of instances into account. We address this challenge by presenting a new active learning framework which considers uncertainty, representativeness, and diversity creation. The proposed approach provides a systematic way for measuring and combining the uncertainty, representativeness, and diversity of an instance. Firstly, use instances' uncertainty and representativeness to constitute the most informative set. Then, use the kernel k-means clustering algorithm to filter the redundant samples and the resulting samples are queried for labels. Extensive experimental results show that the proposed approach outperforms several state-of-the-art active learning approaches.

The uncracked pieces in database cracking

Database cracking has been an area of active research in recent years. The core idea of database cracking is to create indexes adaptively and incrementally as a side-product of query processing. Several works have proposed different cracking techniques for different aspects including updates, tuple-reconstruction, convergence, concurrency-control, and robustness. However, there is a lack of any comparative study of these different methods by an independent group. In this paper, we conduct an experimental study on database cracking. Our goal is to critically review several aspects, identify the potential, and propose promising directions in database cracking. With this study, we hope to expand the scope of database cracking and possibly leverage cracking in database engines other than MonetDB. We repeat several prior database cracking works including the core cracking algorithms as well as three other works on convergence (hybrid cracking), tuple-reconstruction (sideways cracking), and robustness (stochastic cracking) respectively. We evaluate these works and show possible directions to do even better. We further test cracking under a variety of experimental settings, including high selectivity queries, low selectivity queries, and multiple query access patterns. Finally, we compare cracking against different sorting algorithms as well as against different main-memory optimised indexes, including the recently proposed Adaptive Radix Tree (ART). Our results show that: (i) the previously proposed cracking algorithms are repeatable, (ii) there is still enough room to significantly improve the previously proposed cracking algorithms, (iii) cracking depends heavily on query selectivity, (iv) cracking needs to catch up with modern indexing trends, and (v) different indexing algorithms have different indexing signatures.

Design and evaluation of storage organizations for read-optimized main memory databases

Existing main memory data processing systems employ a variety of storage organizations and make a number of storage-related design choices. The focus of this paper is on systematically evaluating a number of these key storage design choices for main memory analytical (i.e. read-optimized) database settings. Our evaluation produces a number of key insights: First, it is always beneficial to organize data into self-contained memory blocks rather than large files. Second, both column-stores and row-stores display performance advantages for different types of queries, and for high performance both should be implemented as options for the tuple-storage layout. Third, cache-sensitive B+-tree indices can play a major role in accelerating query performance, especially when used in a block-oriented organization. Finally, compression can also play a role in accelerating query performance depending on data distribution and query selectivity.

Query Access Assurance in Outsourced Databases

Query execution assurance is an important concept in defeating lazy servers in the database as a service model. We show that extending query execution assurance to outsourced databases with multiple data owners is highly inefficient. To cope with lazy servers in the distributed setting, we propose query access assurance (Qaa) that focuses on IO-bound queries. The goal in Qaa is to enable clients to verify that the server has honestly accessed all records that are necessary to compute the correct query answer, thus eliminating the incentives for the server to be lazy if the query cost is dominated by the IO cost in accessing these records. We formalize this concept for distributed databases, and present two efficient schemes that achieve Qaa with high success probabilities. The first scheme is simple to implement and deploy, but may incur excessive server to client communication cost and verification cost at the client side, when the query selectivity or the database size increases. The second scheme is more involved, but successfully addresses the limitation of the first scheme. Our design employs a few number theory techniques. Extensive experiments demonstrate the efficiency, effectiveness, and usefulness of our schemes.

Continuous sampling from distributed streams

A fundamental problem in data management is to draw and maintain a sample of a large data set, for approximate query answering, selectivity estimation, and query planning. With large, streaming data sets, this problem becomes particularly difficult when the data is shared across multiple distributed sites. The main challenge is to ensure that a sample is drawn uniformly across the union of the data while minimizing the communication needed to run the protocol on the evolving data. At the same time, it is also necessary to make the protocol lightweight, by keeping the space and time costs low for each participant. In this article, we present communication-efficient protocols for continuously maintaining a sample (both with and without replacement) from k distributed streams. These apply to the case when we want a sample from the full streams, and to the sliding window cases of only the W most recent elements, or arrivals within the last w time units. We show that our protocols are optimal (up to logarithmic factors), not just in terms of the communication used, but also the time and space costs for each participant.

Selecting queries from sample to crawl deep web data sources

This paper studies the problem of selecting queries to efficiently crawl a deep web data source using a set of sample documents. Crawling deep web is the process of collecting data from search interfaces by issuing queries. One of the major challenges in crawling deep web is the selection of the queries so that most of the data can be retrieved at a low cost. We propose to learn a set of queries from a sample of the data source. To verify that the queries selected from a sample also produce a good result for the entire data source, we carried out a set of experiments on large corpora including Gov2, newsgroups, wikipedia and Reuters. We show that our sampling-based method is effective by empirically proving that 1 The queries selected from samples can harvest most of the data in the original database; 2 The queries with low overlapping rate in samples will also result in a low overlapping rate in the original database; and 3 The size of the sample and the size of the terms from where to select the queries do not need to be very large. Compared with other query selection methods, our method obtains the queries by analyzing a small set of sample documents, instead of learning the next best query incrementally from all the documents matched with previous queries.

Interactive Ontology Debugging: Two Query Strategies for Efficient Fault Localization

Effective debugging of ontologies is an important prerequisite for their broad application, especially in areas that rely on everyday users to create and maintain knowledge bases, such as the Semantic Web. In such systems ontologies capture formalized vocabularies of terms shared by its users. However in many cases users have different local views of the domain, i.e. of the context in which a given term is used. Inappropriate usage of terms together with natural complications when formulating and understanding logical descriptions may result in faulty ontologies. Recent ontology debugging approaches use diagnosis methods to identify causes of the faults. In most debugging scenarios these methods return many alternative diagnoses, thus placing the burden of fault localization on the user. This paper demonstrates how the target diagnosis can be identified by performing a sequence of observations, that is, by querying an oracle about entailments of the target ontology. To identify the best query we propose two query selection strategies: a simple split-in-half strategy and an entropy-based strategy. The latter allows knowledge about typical user errors to be exploited to minimize the number of queries. Our evaluation showed that the entropy-based method significantly reduces the number of required queries compared to the split-in-half approach. We experimented with different probability distributions of user errors and different qualities of the apriori probabilities. Our measurements demonstrated the superiority of entropy-based query selection even in cases where all fault probabilities are equal, i.e. where no information about typical user errors is available.

Group-Based Active Query Selection for Rapid Diagnosis in Time-Critical Situations

In applications such as active learning and disease/fault diagnosis, one often encounters the problem of identifying an unknown object through a minimal number of queries. This problem has been referred to as query learning or object/entity identification. We consider three extensions of this fundamental problem that are motivated by practical considerations in real-world,time-critical identification tasks such as emergency response. First, we consider the problem where the objects are partitioned into groups, and the goal is to identify only the group to which the object belongs. Second, we address the situation where the queries are partitioned into groups, and an algorithm may suggest a group of queries to a human user, who then selects the actual query. Third, we consider the problem of object identification in the presence of persistent query noise, and relate it to group identification. To address these problems we show that a standard algorithm for object identification, known as generalized binary search, may be viewed as a generalization of Shannon-Fano coding. We then extend this result to the group-based settings, leading to new algorithms, whose performance is demonstrated through a logarithmic approximation bound, and through experiments on simulated data and a database used for toxic chemical identification.

Asymptotically error-optimal shape of sampling zone for query selectivity estimation method based on discrete cosine transform

The problem of query selectivity estimation for database queries is critical for efficient query execution by database management systems. A query execution method strongly depends on early estimated size of a query result. This estimation determines a data access method used later during the query execution. The selectivity parameter is a fraction of table rows that satisfy a single-table query condition. For a selection condition of a range query where an attribute has a continuous domain, the selectivity is equivalent to a definite integral form probability density function (PDF) of attribute values distribution. For a compound selection condition based on many attributes we need a multidimensional space-efficient non-parametric estimator of multivariate PDF of attribute values distribution. A known approach based on Discrete Cosine Transform (DCT) spectrum as an representation of multidimensional PDF is considered. The energy compaction property of DCT lets omit a region of spectrum coefficients with small absolute values without significant losing an accuracy of selectivity estimation. An area of relevant spectrum coefficients is called a sampling zone. Results of experiments from previous works shows that applying the reciprocal shape of the sampling zone gives the least selectivity estimation error subject to a predetermined size of the zone. The main result of this work is a theoretical confirmation of only experimental results from previous works. The paper presents the proof of the theorem that the reciprocal shape of the sampling zone is asymptotically error-optimal. The proof is based on calculus of variations and the isoperimetric problem.

Interactive ontology debugging: Two query strategies for efficient fault localization

Effective debugging of ontologies is an important prerequisite for their broad application, especially in areas that rely on everyday users to create and maintain knowledge bases, such as the Semantic Web. In such systems ontologies capture formalized vocabularies of terms shared by its users. However in many cases users have different local views of the domain, i.e. of the context in which a given term is used. Inappropriate usage of terms together with natural complications when formulating and understanding logical descriptions may result in faulty ontologies. Recent ontology debugging approaches use diagnosis methods to identify causes of the faults. In most debugging scenarios these methods return many alternative diagnoses, thus placing the burden of fault localization on the user. This paper demonstrates how the target diagnosis can be identified by performing a sequence of observations, that is, by querying an oracle about entailments of the target ontology. To identify the best query we propose two query selection strategies: a simple “split-in-half” strategy and an entropy-based strategy. The latter allows knowledge about typical user errors to be exploited to minimize the number of queries. Our evaluation showed that the entropy-based method significantly reduces the number of required queries compared to the “split-in-half” approach. We experimented with different probability distributions of user errors and different qualities of the apriori probabilities. Our measurements demonstrated the superiority of entropy-based query selection even in cases where all fault probabilities are equal, i.e. where no information about typical user errors is available.