Parallel-Correctness and Transferability for Conjunctive Queries

Ameloot, Tom J.; Geck, Gaetano; Ketsman, Bas; Neven, Frank; Schwentick, Thomas

doi:10.1145/2745754.2745759

Cited by 17 publications

(46 citation statements)

References 21 publications

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“…We denote by S +V the VSet-automaton obtained from S by adding self-loops labeled with ⊢ and ⊣ to every state of S for ∈ V . That is, for every state q of S, add the transitions (q, ⊢, q) and (q, ⊣ , q) for Figure 2: Visualization of the splittability condition (2).…”

Section: Splittabilitymentioning

confidence: 99%

“…Per definition (3) implies (1). Thus we need to prove that (1) implies (2) and that (2) implies (3).…”

Section: Splittabilitymentioning

confidence: 99%

“…This contradicts the disjointness of S. By the same argument, the two runs can also not disagree on (3). However, per construction of A S the two runs can also not disagree on (2), as this transition must be used to read the last (⊣ , 1)-symbol of r ′ . Thus there can only be one accepting run for every r ′ ∈ L(A S ).…”

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confidence: 99%

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Split-Correctness in Information Extraction

Doleschal

Kimelfeld

Martens

et al. 2019

Proceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

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Programs for extracting structured information from text, namely information extractors, often operate separately on document segments obtained from a generic splitting operation such as sentences, paragraphs, k-grams, HTTP requests, and so on. An automated detection of this behavior of extractors, which we refer to as splitcorrectness, would allow text analysis systems to devise query plans with parallel evaluation on segments for accelerating the processing of large documents. Other applications include the incremental evaluation on dynamic content, where re-evaluation of information extractors can be restricted to revised segments, and debugging, where developers of information extractors are informed about potential boundary crossing of different semantic components.We propose a new formal framework for split-correctness within the formalism of document spanners. Our preliminary analysis studies the complexity of split-correctness over regular spanners. We also discuss different variants of split-correctness, for instance, in the presence of black-box extractors with "split constraints".

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Section: Splittabilitymentioning

confidence: 99%

“…Per definition (3) implies (1). Thus we need to prove that (1) implies (2) and that (2) implies (3).…”

Section: Splittabilitymentioning

confidence: 99%

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Split-Correctness in Information Extraction

Doleschal

Kimelfeld

Martens

et al. 2019

Proceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems

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“…Large-scale parallel query processing raises many other (practical and foundational) research questions. As an example, recent frameworks for parallel query optimization need to be extended to the multi-round case [11].…”

Section: Managing Data At Scalementioning

confidence: 99%

Research Directions for Principles of Data Management (Abridged)

Abiteboul¹,

Arenas²,

Barceló³

et al. 2017

SIGMOD Rec.

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Research directions for Principles of Data ManagementPDM played a foundational role in the relational database model, with the robust connection between algebraic and calculus-based query languages, the connection between integrity constraints and database design, key insights for the field of query optimization, and the fundamentals of consistent concurrent transactions. This early work included rich cross-fertilization between PDM and other disciplines in mathematics and computer science, including logic, complexity theory, and knowledge representation. Since the 1990s we have seen an overwhelming increase in both the production of data and the ability to store and access such data. This has led to a phenomenal metamorphosis in the ways that we manage and use data. During this time, we have gone (1) from stand-alone disk-based databases to data that is spread across and linked by the Web, (2) from rigidly structured towards loosely structured data, and (3) from relational data to many different data models (hierarchical, graph-structured, data points, NoSQL, text data, image data, etc.). Research on PDM has developed during that time, too, following, accompanying and influencing this process. It has intensified research on extensions of the relational model (data exchange, incomplete data, probabilistic data, . . . ), on other data models (hierachical, semi-structured, graph, text, . . . ), and on a variety of further data management areas, including knowledge representation and the semantic web, data privacy and security, and data-aware (business) processes. Along the way, the PDM community expanded its cross-fertilization with related areas, to include automata theory, web services, parallel computation, document processing, data structures, scientific workflow, business process management, data-centered dynamic systems, data mining, machine learning, information extraction, etc.Looking forward, three broad areas of data management stand out where principled, mathematical thinking can bring new approaches and much-needed clarity. The first relates to the full lifecycle of so-called "Big Data Analytics", that is, the application of statistical and machine learning techniques to make sense out of, and derive value from, massive volumes of data. The second stems from new forms of data creation and processing, especially as it arises in applications such as web-based commerce, social media applications, and dataaware workflow and business process management. The third, which is just beginning to emerge, is the development of new principles and approaches in support of ethical data management. We briefly illustrate some of the primary ways that these three areas can be supported by the seven PDM research themes that are explored in this report.The overall lifecycle of Big Data Analytics raises a wealth of challenge areas that PDM can help with. As documented in numerous sources, so-called "data wrangling" can form 50% to 80% of the labor costs in an analytics investigation. The challenges of data wrangling can be ...

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“…Thus, minimization of those parameters is a very attractive algorithm property and present a trade-off with accuracy of the intersection set size. Related work and our contribution: Conjunctive queries over distributed databases were extensively researched (see as an example [24,26,12,3,42]).…”

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confidence: 99%

Efficient and private approximations of distributed databases calculations

Derbeko

Gudes

Ullman

2017

2017 IEEE International Conference on Big Data (Big Data)

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In recent years, an increasing amount of data is collected in different and often, not cooperative, databases. The problem of privacy-preserving, distributed calculations over separated databases and, a relative to it, issue of private data release were intensively investigated. However, despite a considerable progress, computational complexity, due to an increasing size of data, remains a limiting factor in real-world deployments, especially in case of privacy-preserving computations.In this paper, we present a general method for trade off between performance and accuracy of distributed calculations by performing data sampling. Sampling was a topic of extensive research that recently received a boost of interest. We provide a sampling method targeted at separate, non-collaborating, vertically partitioned datasets. The method is exemplified and tested on approximation of intersection set both without and with privacy-preserving mechanism. An analysis of the bound on error as a function of the sample size is discussed and heuristic algorithm is suggested to further improve the performance. The algorithms were implemented and experimental results confirm the validity of the approach. arXiv:1605.06143v1 [cs.DB] 19 May 2016 We assume that the number of records that are not present in all datasets is very small compared to the total number of records and thus, for the sake of simplicity, the data providers in DP are assumed to contain the same records with size denoted by N = |D|, i.e. vertical split of the data. The size of the intersection might not be calculated precisely, but up to a given accuracy provided by the client querying the data providers. For example, the client that performs a query is interested in an integer percentage of people from the entire population having both criminal record and specific genetic mutation, i.e. the results can be rounded to a closest percentage. Intuitively, this relaxed requirement should result in more computationally and network efficient algorithms. The optimization of computation time and network traffic are important, as mentioned above, in the commonly used public-cloud deployment; the user is charged for computation time (also called CPU time) and network traffic. Thus, minimization of those parameters is a very attractive algorithm property and present a trade-off with accuracy of the intersection set size. Related work and our contribution: Conjunctive queries over distributed databases were extensively researched (see as an example [24,26,12,3,42]).

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Parallel-Correctness and Transferability for Conjunctive Queries

Cited by 17 publications

References 21 publications

Split-Correctness in Information Extraction

Split-Correctness in Information Extraction

Research Directions for Principles of Data Management (Abridged)

Efficient and private approximations of distributed databases calculations

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