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Register a new user80 New Packages to Mine Database Query Logs
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Added by
Thibault Sellam
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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The query log of a DBMS is a powerful resource. It enables many practical applications, including query optimization and user experience enhancement. And yet, mining SQL queries is a difficult task. The fundamental problem is that queries are symbolic objects, not vectors of numbers. Therefore, many popular statistical concepts, such as means, regression, or decision trees do not apply. Most authors limit themselves to ad hoc algorithms or approaches based on neighborhoods, such as k Nearest Neighbors. Our project is to challenge this limitation. We introduce methods to manipulate SQL queries as if they were vectors, thereby unlocking the whole statistical toolbox. We present three families of methods: feature maps, kernel methods, and Bayesian models. The first technique directly encodes queries into vectors. The second one transforms the queries implicitly. The last one exploits probabilistic graphical models as an alternative to vector spaces. We present the benefits and drawbacks of each solution, highlight how they relate to each other, and make the case for future investigation.
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Interactive tools make data analysis more efficient and more accessible to end-users by hiding the underlying query complexity and exposing interactive widgets for the parts of the query that matter to the analysis. However, creating custom tailored (i.e., precise) interfaces is very costly, and automated approaches are desirable. We propose a syntactic approach that uses queries from an analysis to generate a tailored interface. We model interface widgets as functions I(q) -> q that modify the current analysis query $q$, and interfaces as the set of queries that its widgets can express. Our system, Precision Interfaces, analyzes structural changes between input queries from an analysis, and generates an output interface with widgets to express those changes. Our experiments on the Sloan Digital Sky Survey query log suggest that Precision Interfaces can generate useful interfaces for simple unanticipated tasks, and our optimizations can generate interfaces from logs of up to 10,000 queries in <10s.
Interactive tools make data analysis both more efficient and more accessible to a broad population. Simple interfaces such as Google Finance as well as complex visual exploration interfaces such as Tableau are effective because they are tailored to the desired user tasks. Yet, designing interactive interfaces requires technical expertise and domain knowledge. Experts are scarce and expensive, and therefore it is currently infeasible to provide tailored (or precise) interfaces for every user and every task. We envision a data-driven approach to generate tailored interactive interfaces. We observe that interactive interfaces are designed to express sets of programs; thus, samples of programs-increasingly collected by data systems-may help us build interactive interfaces. Based on this idea, Precision Interfaces is a language-agnostic system that examines an input query log, identifies how the queries structurally change, and generates interactive web interfaces to express these changes. The focus of this paper is on applying this idea towards logs of structured queries. Our experiments show that Precision Interfaces can support multiple query languages (SQL and SPARQL), derive Tableaus salient interaction components from OLAP queries, analyze <75k queries in <12 minutes, and generate interaction designs that improve upon existing interfaces and are comparable to human-crafted interfaces.
The galaxy database GOLDmine (http://goldmine.mib.infn.it/) has been significantly updated (Sept/1/2003) The new features include: a) Sample extension:the GOLDmine sample has been extended from the original Virgo cluster + Coma supercluster regions to include the clusters: A262, Cancer, A2147, A2151, A2197, A2199. 382 galaxies from the GCGC (with m_p<15.7) have been added in these regions. b) New query keys: 1) query by near position (and near name). 2) query by available images. c) Routinary image update: 1) 59 (B). 72 (V) and 70 (H_alpha) new frames from observations carried on by the GOLDmine team in spring 2003. 2) 157 new optical (drift-scan) spectra from observations carried on by the GOLDmine team in 2002-2003. 3) 225 B frames of VCC galaxies taken with the INT (kindly provided by S. Sabatini). 4) 56 B frames of galaxies in A1367 taken with the CFHT (kindly provided by M. Treyer). 5) 20 (H), 32 (K) band frames of bright Virgo members (from 2MASS). The new numbers in GOLDmine are: 3649 galaxies, 706 V-band frames, 858 B-band frames, 385 H_alpha frames (NET), 385 H_alpha frames (OFF-band), 1241 H-band frames, 114 K-band frames, 323 Spectra All frames are available in FITS (and jpg) format.
The database systems course is offered as part of an undergraduate computer science degree program in many major universities. A key learning goal of learners taking such a course is to understand how SQL queries are processed in a RDBMS in practice. Since a query execution plan (QEP) describes the execution steps of a query, learners can acquire the understanding by perusing the QEPs generated by a RDBMS. Unfortunately, in practice, it is often daunting for a learner to comprehend these QEPs containing vendor-specific implementation details, hindering her learning process. In this paper, we present a novel, end-to-end, generic system called lantern that generates a natural language description of a qep to facilitate understanding of the query execution steps. It takes as input an SQL query and its QEP, and generates a natural language description of the execution strategy deployed by the underlying RDBMS. Specifically, it deploys a declarative framework called pool that enables subject matter experts to efficiently create and maintain natural language descriptions of physical operators used in QEPs. A rule-based framework called RULE-LANTERN is proposed that exploits pool to generate natural language descriptions of QEPs. Despite the high accuracy of RULE-LANTERN, our engagement with learners reveal that, consistent with existing psychology theories, perusing such rule-based descriptions lead to boredom due to repetitive statements across different QEPs. To address this issue, we present a novel deep learning-based language generation framework called NEURAL-LANTERN that infuses language variability in the generated description by exploiting a set of paraphrasing tools and word embedding. Our experimental study with real learners shows the effectiveness of lantern in facilitating comprehension of QEPs.
Digital multimedia watermarking technology was suggested in the last decade to embed copyright information in digital objects such images, audio and video. However, the increasing use of relational database systems in many real-life applications created an ever increasing need for watermarking database systems. As a result, watermarking relational database systems is now merging as a research area that deals with the legal issue of copyright protection of database systems. Approach: In this study, we proposed an efficient database watermarking algorithm based on inserting binary image watermarks in non-numeric mutli-word attributes of selected database tuples. Results: The algorithm is robust as it resists attempts to remove or degrade the embedded watermark and it is blind as it does not require the original database in order to extract the embedded watermark. Conclusion: Experimental results demonstrated blindness and the robustness of the algorithm against common database attacks.
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