Any important data management and analytics tasks cannot be completely addressed by automated processes. These tasks, such as entity resolution, sentiment analysis, and image recognition can be enhanced through the use of human cognitive ability. Crowdsouring platforms are an effective way to harness the capabilities of people (i.e., the crowd) to apply human computation for such tasks. Thus, crowdsourced data management has become an area of increasing interest in research and industry. We identify three important problems in crowdsourced data management. (1) Quality Control: Workers may return noisy or incorrect results so effective techniques are required to achieve high quality; (2) Cost Control: The crowd is not free, and cost control aims to reduce the monetary cost; (3) Latency Control: The human workers can be slow, particularly compared to automated computing time scales, so latency-control techniques are required. There has been significant work addressing these three factors for designing crowdsourced tasks, developing crowdsourced data manipulation operators, and optimizing plans consisting of multiple operators. In this paper, we survey and synthesize a wide spectrum of existing studies on crowdsourced data management. Based on this analysis we then outline key factors that need to be considered to improve crowdsourced data management.

/pdf/crowdsourced-data-management-a-survey-1a5fjazc94.pdf

Crowdsourced Data Management: A Survey

Bidirectional transformations provide a novel mechanism for synchronizing and maintaining the consistency of information between input and output. Despite many promising results on bidirectional transformations, these have been limited to the context of relational or XML (tree-like) databases. We challenge the problem of bidirectional transformations within the context of graphs, by proposing a formal definition of a well-behaved bidirectional semantics for UnCAL, i.e., a graph algebra for the known UnQL graph query language. The key to our successful formalization is full utilization of both the recursive and bulk semantics of structural recursion on graphs. We carefully refine the existing forward evaluation of structural recursion so that it can produce sufficient trace information for later backward evaluation. We use the trace information for backward evaluation to reflect in-place updates and deletions on the view to the source, and adopt the universal resolving algorithm for inverse computation and the narrowing technique to tackle the difficult problem with insertion. We prove our bidirectional evaluation is well-behaved. Our current implementation is available online and confirms the usefulness of our approach with nontrivial applications.

/pdf/bidirectionalizing-graph-transformations-4uijlmafns.pdf

Bidirectionalizing graph transformations

Many important data management and analytics tasks cannot be completely addressed by automated processes. Crowdsourcing is an effective way to harness human cognitive abilities to process these computer-hard tasks, such as entity resolution, sentiment analysis, and image recognition. Crowdsourced data management has been extensively studied in research and industry recently. In this tutorial, we will survey and synthesize a wide spectrum of existing studies on crowdsourced data management. We first give an overview of crowdsourcing, and then summarize the fundamental techniques, including quality control, cost control, and latency control, which must be considered in crowdsourced data management. Next we review crowdsourced operators, including selection, collection, join, top-k, sort, categorize, aggregation, skyline, planning, schema matching, mining and spatial crowdsourcing. We also discuss crowdsourcing optimization techniques and systems. Finally, we provide the emerging challenges.

Crowdsourced Data Management: Overview and Challenges

Sequential Tests of Statistical Hypotheses.

Property graphs have reached a high level of maturity, witnessed by multiple robust graph database systems as well as the ongoing ISO standardization effort aiming at creating a new standard Graph Query Language (GQL). Yet, despite documented demand, schema support is limited both in existing systems and in the first version of the GQL Standard. It is anticipated that the second version of the GQL Standard will include a rich DDL. Aiming to inspire the development of GQL and enhance the capabilities of graph database systems, we propose PG-Schema, a simple yet powerful formalism for specifying property graph schemas. It features PG-Schema with flexible type definitions supporting multi-inheritance, as well as expressive constraints based on the recently proposed PG-Keys formalism. We provide the formal syntax and semantics of PG-Schema, which meet principled design requirements grounded in contemporary property graph management scenarios, and offer a detailed comparison of its features with those of existing schema languages and graph database systems.

/pdf/pg-schema-schemas-for-property-graphs-1qu4j0wq.pdf

PG-Schema: Schemas for Property Graphs

We study in this paper the computation of skyline queries - a popular tool for multicriteria data analysis - in the presence of noisy input. Motivated by crowdsourcing applications, we present the first algorithms for skyline evaluation in a computation model where the input data items can only be compared through noisy comparisons. In this model comparisons may return wrong answers with some probability, and confidence can be increased through independent repetitions of a comparison. Our goal is to minimize the number of comparisons required for computing or verifying a candidate skyline, while returning the correct answer with high probability. We design output-sensitive algorithms, namely algorithms that take advantage of the potentially small size of the skyline, and analyze the number of comparison rounds of our solutions. We also consider the problem of predicting the most likely skyline given some partial information in the form of noisy comparisons, and show that optimal prediction is computationally intractable.

/pdf/skyline-queries-with-noisy-comparisons-4cae57w5zd.pdf

Skyline Queries with Noisy Comparisons

We study the problem of update translation for views on XML documents More precisely, given an XML view definition and a user defined view update program, find a source update program that translates the view update without side effects on the view Additionally, we require the translation to be defined on all possible source documents; this corresponds to Hegner's notion of uniform translation The existence of such translation would allow to update XML views without the need of materializationThe class of views we consider can remove parts of the document and rename nodes Our update programs define the simultaneous application of a collection of atomic update operations among insertion/deletion of a subtree and node renaming Such update programs are compatible with the XQuery Update Facility (XQUF) snapshot semantics Both views and update programs are represented by recognizable tree languages We present as a proof of concept a small fragment of XQUF that can be expressed by our update programs, thus allows for update propagationTwo settings for the update problem are studied: without source constraints, where all source updates are allowed, and with source constraints, where there is a restricted set of authorized source updates Using tree automata techniques, we establish that without constraints, all view updates are uniformly translatable and the translation is tractable In presence of constraints, not all view updates are uniformly translatable However, we introduce a reasonable restriction on update programs for which uniform translation with constraints becomes possible

/pdf/view-update-translation-for-xml-2nr881lqlt.pdf

View update translation for XML

In this paper, we revisit the view based security framework for XML without imposing any of the previously considered restrictions on the class of queries, the class of DTDs, and the type of annotations used to define the view. First, we show that the full class of Regular XPath queries is closed under query rewriting . Next, we address the problem of constructing a DTD that describes the view schema, which in general needs not be regular. We propose three different methods of approximating the view schema and we show that the produced DTDs are indistinguishable from the exact schema (with queries from a class specific for each method). Finally, we investigate problems of static analysis of security access specifications.

/pdf/xml-security-views-revisited-2o1l39f070.pdf

XML Security Views Revisited

We study the problem of update propagation across views in the setting where both the view and the source database are XML documents. We consider a simple class of XML views that remove selected parts of the source document. The considered update operations permit to insert and delete subtrees of the document. We focus on constructing propagations that are 1) schema compliant i.e., when applied to the source document they give a document that satisfies the document schema; 2) side-effect free i.e., the view of the new source document is exactly as the result of applying the user update to the old view. We present a special structure allowing to capture all such propagations. We also show how to use this structure to capture only those propagations that affect minimally the parts of the document which are not visible in the view. Finally, we present a general outline of a polynomial algorithm constructing a unique propagation.

/pdf/the-view-update-problem-for-xml-1no7d4mehq.pdf

The view update problem for XML

We investigate the problem of constructing a shape graph that describes the structure of a given graph database. We employ the framework of grammatical inference , where the objective is to find an inference algorithm that is both sound , i.e., always producing a schema that validates the input graph, and complete , i.e., able to produce any schema, within a given class of schemas, provided that a sufficiently informative input graph is presented. We identify a number of fundamental limitations that preclude feasible inference. We present inference algorithms based on natural approaches that allow to infer schemas that we argue to be of practical importance.

Inference of Shape Graphs for Graph Databases

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