Wen-chi Hou

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Papers by Wen-chi Hou

A Hierarchical Precedence Concurrency Control Protocol for High Data Contention Database Environments

In this paper, we propose a family of concurrency control protocols, called the Hierarchical Prec... more In this paper, we propose a family of concurrency control protocols, called the Hierarchical Precedence Concurrency Control (HPCC) protocols, for high data contention database environments. HPCC attempts to be more aggressive by permitting more serializable schedules than the two-phase locking (2PL). It maintains cycle-free precedence hierarchies for conflicting transactions. Conflicting operations are allowed to proceed only if the hierarchical orderings of precedence is not violated. Transactions commit based on the serialization order established during the executions. A detailed simulation model has been implemented and extensive experiments have been conducted to evaluate the performance of the proposed approach. The results demonstrate that the proposed algorithm outperforms the two-phase locking over a wide range of system workloads.

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Estimate query results within limited time constraints is a challenging problem in the research o... more Estimate query results within limited time constraints is a challenging problem in the research of big data management. Query estimation based on simple random samples per- forms well for simple selection queries; however, return results with extremely high relative errors for complex join queries. Existing methods only work well with foreign key joins, and the sample size can grow dramatically as the dataset gets larger. This research implements a scalable sampling scheme in a big data environment, namely correlated sampling in map-reduce, that can speed up search query length results, give precise join query estimations, and minimize storage costs when presented with big data. Extensive experiments with large TPC-H datasets in Apache Hive show that our sampling method produces fast and accurate query estimations on big data.

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An Empirical Study of Qualities of Association Rules from a Statistical View Point

Journal of Information Processing Systems, 2008

Minimum support and confidence have been used as criteria for generating association rules in all... more Minimum support and confidence have been used as criteria for generating association rules in all association rule mining algorithms. These criteria have their natural appeals, such as simplicity; few researchers have suspected the quality of generated rules. In this paper, we examine the rules from a more rigorous point of view by conducting statistical tests. Specifically, we use contingency tables and chi-square test to analyze the data. Experimental results show that one third of the association rules derived based on the support and confidence criteria are not significant, that is, the antecedent and consequent of the rules are not correlated. It indicates that minimum support and minimum confidence do not provide adequate discovery of meaningful associations. The chi-square test can be considered as an enhancement or an alternative solution.

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Approximate Clustering on Data Streams Using Discrete Cosine Transform

Journal of Information Processing Systems, 2010

In this study, a clustering algorithm that uses DCT transformed data is presented. The algorithm ... more In this study, a clustering algorithm that uses DCT transformed data is presented. The algorithm is a grid density-based clustering algorithm that can identify clusters of arbitrary shape. Streaming data are transformed and reconstructed as needed for clustering. Experimental results show that DCT is able to approximate a data distribution efficiently using only a small number of coefficients and preserve the clusters well. The grid based clustering algorithm works well with DCT transformed data, demonstrating the viability of DCT for data stream clustering applications.

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Quality of Association Rules by Chi-Squared Test

Encyclopedia of Data Warehousing and Mining, Second Edition

Mining market basket data (Agrawal et al. 1993, Agrawal et al. 1994) has received a great deal of... more Mining market basket data (Agrawal et al. 1993, Agrawal et al. 1994) has received a great deal of attention in the recent past, partly due to its utility and partly due to the research challenges it presents. Market basket data typically consists of store items purchased on a per-transaction basis, but it may also consist of items bought by a customer over a period of time. The goal is to discover buying patterns, such as two or more items that are often bought together. Such finding could aid in marketing promotions and customer relationship management. Association rules reflect a fundamental class of patterns that exist in the data. Consequently, mining association rules in market basket data has become one of the most important problems in data mining. Agrawal et al. (Agrawal, et al. 1993, Agrawal et al. 1994) have provided the initial foundation for this research problem. Since then, there has been considerable amount of work (Bayardo et al. 1999, Bayardo et al. 1999, Brin et al...

Implementation and evaluation of relational algebra operations on the Connection Machine

Conference on Parallel andDistributed Information Systems, 1993

The authors describe the implementation and analysis of relational algebra (RA) operators on a si... more

Relational aggregate query processing techniques for real-time databases

In this thesis, a new query processing technique is proposed to cut down systematically the time ... more In this thesis, a new query processing technique is proposed to cut down systematically the time involved in processing aggregate relational algebra queries in relational databases. The author uses data samples and statistical methods to construct an approximate response to a given aggregate query when there is not enough time to completely process the query. This thesis considers only COUNT(E) type queries, where E is an arbitrary relational algebra expression. He makes no assumptions about the distributions of attribute values and ordering of tuples in the input relations, and propose consistent and unbiased estimators for arbitrary COUNT(E) type queries. A sampling plan based on the cluster sampling method is designed to improve the utilization of sampled data and to reduce the cost of sampling. He iteratively determines the appropriate amount of sample tuples from each relation such that an aggregate query with the set of samples as input can be evaluated within the given amount...

Holistic Boolean-Twig Pattern Matching for Efficient XML Query Processing

IEEE Transactions on Knowledge and Data Engineering, 2012

Twig pattern matching is a critical operation for XML query processing, and the holistic computin... more Twig pattern matching is a critical operation for XML query processing, and the holistic computing approach has shown superior performance over other methods. Since Bruno et al. introduced the first holistic twig join algorithm, T wigStack, numerous socalled holistic twig join algorithms have been proposed. Yet practical XML queries often require support for more general twig patterns, such as the ones that allow arbitrary occurrences of an arbitrary number of logical connectives (AND, OR, and NOT); such types of twigs are referred to as B-twigs (i.e., Boolean-Twigs) or AND/OR/NOT-twigs. We have seen interesting work on generalizing the holistic twig join approach to AND/OR-twigs and AND/NOT-twigs, but have not seen any further effort addressing the problem of AND/ OR/NOT-Twigs at the full scale, which therefore forms the main theme of this paper. In this paper, we investigate novel mechanisms for efficient B-twig pattern matching. In particular, we introduce "B-twig normalization" as an important first-step in our approach toward eventually conquering the complexity of B-twigs, and then present BT wigMerge-the first holistic twig join algorithm designed for Btwigs. Both analytical and experimental results show that BT wigMerge is optimal for B-twig patterns with AD (Ancestor-Descendant) edges and/or PC (Parent-Child) edges.

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An approach of composing near optimal invalidation reports

Proceedings of the 6th international conference on Mobile data management, 2005

Caching can reduce expensive data transfers and improve the performance of mobile computing. In o... more Caching can reduce expensive data transfers and improve the performance of mobile computing. In order to reuse caches after short disconnections, invalidation reports are broadcast to clients to identify outdated items. Detailed reports may not be desirable because they can consume too much bandwidth. On the other hand, false invalidations may set in if the accurate timing of updates is not provided. In this research, we aim to reduce the false invalidation rates of cached items. Based on our analysis, false invalidation rates are closely related to clients' reconnection patterns (i.e., the distribution of the time spans between disconnections and reconnections). We show that in theory for any given reconnection pattern, a report with a minimal false invalidation rate can be derived. For practical uses, we propose to capture the reconnection pattern by sampling and develop a method to compose a near-optimal invalidation report. This method is simple and fast. Experimental results have confirmed that our method is indeed more effective in reducing the false invalidation rate than others.

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Sample Trace: Deriving Fast Approximation for Repetitive Queries

Repetitive queries refer to those queries that are likely to be executed repeatedly in the future... more Repetitive queries refer to those queries that are likely to be executed repeatedly in the future. Queries such as those used to generate periodic reports, perform routine summarization and data analysis belong to this category. Repetitive queries can constitute a large portion of the daily activities of a database system, and thus deserve extra optimization efforts. In this paper, we propose to record information about how tuples are joined in a repetitive query, called the query trace. We prove that the query trace is sufficient to compute the exact selectivities of joins for all plans of a given query. To reduce the space and time overheads in generating the query trace, we propose to construct only a sample of the query trace, called a sample trace, which can be much smaller than a (complete) query trace. A special operation, called a sample outer join, is designed to accomplish this feat. Accurate estimations of join selectivities, with associated confidence intervals, can be d...

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Processing real-time, non-aggregate queries with time-constraints in CASE-DB

[1992] Eighth International Conference on Data Engineering

The problem of time-constrained query evaluation in a single-user database management system (DBM... more The problem of time-constrained query evaluation in a single-user database management system (DBMS) is considered. CASE-DB is a real-time, single user, relational prototype DBMS that uses the relational algebra as its query language. Given a nonaggregate query and a fragment chain for each input relation of the query. CASE-DB uses iterative query evaluation techniques to obtain a response first to

Range-Sum Queries over High Dimensional Data Cubes Using a Dynamic Grid File

Emerging Trends and Approaches

In this chapter, the authors propose to use the grid file to store multi-dimensional data cubes a... more In this chapter, the authors propose to use the grid file to store multi-dimensional data cubes and answer range-sum queries. The grid file is enhanced with a dynamic splitting mechanism to accommodate insertions of data. It overcomes the drawback of the traditional grid file in storing uneven data while enjoying its advantages of simplicity and efficiency. The space requirement grows linearly with the dimension of the data cube, compared with the exponential growth of conventional methods that store pre-computed aggregate values for range-sum queries. The update cost is O(1), much faster than the pre-computed data cube approaches, which generally have exponential update cost. The grid file structure can also respond to range queries quickly. They compare it with an approach that uses the R*-tree structure to store the data cube. The experimental results show that the proposed method performs favorably in file size, update speed, construction time, and query response time for both e...

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Grid File for Efficient Data Cube Storage

Computers and Their Applications, 2006

Performance Analysis of Two-Phase Locking: Data Contention vs. Resource Contention

Computers and Their Applications, 2004

A Framework for Clustering on Data Streams

Computers and Their Applications, 2006

A Data Stream Clustering Algorithm Using Discrete Cosine Transform

Computers and Their Applications, 2008

An indicator device is disclosed that is useful for measuring the level of a material stored in a... more An indicator device is disclosed that is useful for measuring the level of a material stored in a tank. The device includes a stationary tubular section that fastens into the top wall of the tank. One or more tubular sections of smaller diameter are made to slide inside the stationary section to form a telescoping unit. The smallest tubular section has a foot member at its bottom end and a cable fastened to its upper end. The cable passes over a sheave and is wound onto a winch operated by a motor. In operation, the winch lowers the smallest tubular section into the tank until the foot member contacts the material in the tank and compresses the material enough to stop the downward movement of the tubular section. The winch is then reversed to bring the tubular section back to its "up" position. The sheave operates a counting and recording device that records the total distance traveled by the cable, and this distance indicates the level of material in the tank.

A Scientific DBMS for Programmable Logic Controllers

Data Engineering Bulletin is a quarterly publication of the IEEE Computer Society Technical Commi... more Data Engineering Bulletin is a quarterly publication of the IEEE Computer Society Technical Committee on Data Engineering. Its scope of interest includes: data structures and models, access strategies, access control techniques, database architecture, database machines, intelligent front ends, mass storage for very large databases, distributed database systems and techniques, database software design and im plementation, database utilities, database security and related areas. Contribution to the Bulletin is hereby solicited. News items, letters, technical papers, book reviews, meet ing previews, summaries, case studies, etc., should be sent to the Editor. All letters to the Editor will be considered for publication unless accompanied by a request to the contrary. Technical papers are unre fereed. Opinions expressed in contributions are those of the individual author rather than the official position of the TC on Data Engineering, the IEEE Computer Society, or organizations with which the author may be affiliated. Membership in the Data Engineering Technical Committee is open to individuals who demonstrate willingness to actively participate in the various acti vities of the TC. A member of the IEEE Computer Society may join the TC as a full member. A nonmember of the Computer Society may join as a par ticipating member, with approval from at least one officer of the TC. Both full members and participat ing members of the TC are entitled to receive the quarterly bulletin of the TC free of charge, until fur ther notice. Letter from the Editor This issue of Data Engineering Bulletin is devoted to Statistical and Scientific Databases. It contains short versions of six papers and two panel descriptions from the Fifth International Conference on Statistical and Scientific Databases, which was held in Charlotte, North Carolina on April 3-5, 1990. For interested readers the full proceedings of the conference is also available as:

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An Optimal Invalidation Method for Mobile Databases

Computing and Information Technologies, 2001

ABSTRACT

Selectivity Estimation of Range Queries over Data Streams Using Cosine Transform

ABSTRACT

Statistical inference of unknown attribute values in databases

Proceedings of the second international conference on Information and knowledge management - CIKM '93, 1993

In this paper, we propose to use statistical methods to estimate unknown attribute values in data... more In this paper, we propose to use statistical methods to estimate unknown attribute values in databases, as compared to assigning possible values at users' discretion in common practice. Regression models and classification analysis are introduced for estimating continuous and categorical unknown attribute values, respectively. Procedures for selecting relevant attributes in a relation and preliminary experimental results of the proposed models on a real life database are also presented.

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