We are excited to announce the third issue dedicated to the Principles of Database Systems (PODS) research track of the Proceedings of the ACM on Management of Data (PACMMOD) journal. In its current form, the journal hosts both a SIGMOD and a PODS research track. The PODS research track aims to provide a solid scientific foundation for methods, techniques, and solutions to the data management challenges that continually arise in our data-driven society. More specifically, articles in the PODS track of PACMMOD present principled contributions to modeling, application, system building, and both theoretical and experimental validation in the context of data management. Such articles may be based, among others, on establishing theoretical results, developing new concepts and frameworks that merit further exploration, providing experimental work that sheds light on the scientific foundations of the discipline, or conducting a rigorous analysis of widely used and/or recently developed industry artifacts.

This editorial accompanies the third issue dedicated to the PODS research track of the Proceedings of the ACM on Management of Data (PACMMOD) journal. The journal hosts a SIGMOD and a PODS research track. The PODS research track aims to provide a solid scientific basis for methods, techniques, and solutions for the data management challenges that continually arise in our data-driven society. Articles for the PODS track of PACMMOD present principled contributions to modeling, application, system building, and both theoretical and experimental validation in the context of data management. Such articles might be based, among others, on establishing theoretical results, developing new concepts and frameworks that deserve further exploration, providing experimental work that sheds light on the scientific foundations of the discipline, or conducting a rigorous analysis of industry artifacts that are widely used and/or recently developed. Articles published in this track will be invited for presentation to the ACM Symposium on Principles of Database Systems (PODS), which is held jointly with SIGMOD each year.

Quantum computing has emerged as a transformative force in the evolution of computing technology. Recent efforts have applied quantum techniques to classical database challenges, such as query optimization, data integration, index selection, and transaction management. In this paper, we shift focus to a critical yet underexplored area: data management for quantum computing. We are currently in the noisy intermediate-scale quantum (NISQ) era, where qubits, while promising, are fragile and still limited in scale. After differentiating quantum data from classical data, we outline current and future data management paradigms in the NISQ era and beyond. We address the data management challenges arising from the emerging demands of near-term quantum computing. Our goal is to chart a clear course for future quantum-oriented data management research, establishing it as a cornerstone for the advancement of quantum computing in the NISQ era.

This editorial accompanies the second issue dedicated to the PODS research track of the Proceedings of the ACM on Management of Data (PACMMOD) journal. The journal hosts a SIGMOD and a PODS research track. The PODS research track aims to provide a solid scientific basis for methods, techniques, and solutions for the data management challenges that continually arise in our data-driven society. Articles for the PODS track of PACMMOD present principled contributions to modeling, application, system building, and both theoretical and experimental validation in the context of data management. Such articles might be based, among others, on establishing theoretical results, developing new concepts and frameworks that deserve further exploration, providing experimental work that sheds light on the scientific foundations of the discipline, or conducting a rigorous analysis of industry artifacts that are widely used and/or recently developed. Articles published in this track will be invited for presentation to the ACM Symposium on Principles of Database Systems (PODS), which is held jointly with SIGMOD each year.

We are excited to announce the first issue dedicated to the PODS research track of the Proceedings of the ACM on Management of Data, or PACMMOD, journal. In its current form, this new journal hosts a SIGMOD and a PODS research track. The PODS research track aims to provide a solid scientific basis for methods, techniques, and solutions for the data management challenges that continually arise in our data-driven society. Articles for the PODS track of PACMMOD present principled contributions to modeling, application, system building, and both theoretical and experimental validation in the context of data management. Such articles might be based, among others, on establishing theoretical results, developing new concepts and frameworks that deserve further exploration, providing experimental work that sheds light on the scientific foundations of the discipline, or a rigorous analysis of both widely used and recently developed industry artifacts. At a time when computer science is increasingly data centric, it is essential to promote an active exchange of tools and techniques between principles of database systems and other communities focused on data management. The PODS track thus pays special attention to those papers that help in the urgent process of integrating data management techniques within broader computer science. Articles published in this track will be invited for presentation to the ACM Symposium on Principles of Database Systems (PODS), which is held jointly with SIGMOD each year.

A key component of graph and relational learning methods is the computation of vector representations of the input graphs or relations. The starting point of this tutorial is that we model this computation as queries, mapping relational objects into the realm of real vector spaces. We then revisit recent works in the machine learning community on the expressive power of graph learning methods from this unifying query language perspective. Here, we consider the expressive power related to the discrimination of inputs and to the approximation power of functions. Finally, we argue that the bridge between graph learning and query languages opens many interesting avenues for further research.

Linear algebra algorithms often require some sort of iteration or recursion as is illustrated by standard algorithms for Gaussian elimination, matrix inversion, and transitive closure. A key characteristic shared by these algorithms is that they allow looping for a number of steps that is bounded by the matrix dimension. In this paper we extend the matrix query language MATLANG with this type of recursion, and show that this suffices to express classical linear algebra algorithms. We study the expressive power of this language and show that it naturally corresponds to arithmetic circuit families, which are often said to capture linear algebra. Furthermore, we analyze several sub-fragments of our language, and show that their expressive power is closely tied to logical formalisms on semiring-annotated relations.

We investigate when two graphs, represented by their adjacency matrices, can be distinguished by means of sentences formed in MATLANG, a matrix query language which supports a number of elementary linear algebra operators. When undirected graphs are concerned, and hence the adjacency matrices are real and symmetric, precise characterisations are in place when two graphs (i.e., their adjacency matrices) can be distinguished. Turning to directed graphs, one has to deal with asymmetric adjacency matrices. This complicates matters. Indeed, it requires to understand the more general problem of when two arbitrary matrices can be distinguished in MATLANG. We provide characterisations of the distinguishing power of MATLANG on real and complex matrices, and on adjacency matrices of directed graphs in particular. The proof techniques are a combination of insights from the symmetric matrix case and results from linear algebra and linear control theory.

Methods for cleaning dirty data typically employ additional information about the data such as user-provided constraints specifying when data is dirty, e.g., domain restrictions, illegal value combinations, or logical rules. However, real-world scenarios usually only have dirty data available, without known constraints. In such settings, constraints are automatically discovered on dirty data and discovered constraints are used to detect and repair errors. Typical repairing processes stop there. Yet, when constraint discovery algorithms are re-run on the repaired data (assumed to be clean), new constraints and thus errors are often found. The repairing process then introduces new constraint violations. We present a different type of repairing method, which prevents introducing new constraint violations, according to a discovery algorithm. Summarily, our repairs guarantee that all errors identified by constraints discovered on the dirty data are fixed; and the constraint discovery process cannot identify new constraint violations. We do this for a new kind of constraints, called forbidden itemsets (FBIs), capturing unlikely value co-occurrences. We show that FBIs detect errors with high precision. Evaluation on real-world data shows that our repair method obtains high-quality repairs without introducing new FBIs. Optional user interaction is readily integrated, with users deciding how much effort to invest.

Speeding up probabilistic inference remains a key challenge in probabilistic databases (PDBs) and the related area of statistical relational learning (SRL). Since computing probabilities for query answers is #P-hard, even for fairly simple conjunctive queries, both the PDB and SRL communities have proposed a number of approximation techniques over the years. The two prevalent techniques are either (i) MCMC-style sampling or (ii) branch-and-bound (B&B) algorithms that iteratively improve model-based bounds using a combination of variable substitution and elimination. We propose a new anytime B&B approximation scheme that encompasses all prior model-based approximation schemes proposed in the PDB and SRL literature. Our approach relies on the novel idea of "scaled dissociation" which can improve both the upper and lower bounds of existing modelbased algorithms. We apply our approach to the well-studied problem of evaluating self-join-free conjunctive queries over tuple-independent PDBs, and show a consistent reduction in approximation error in our experiments on TPC-H, Yago3, and a synthetic benchmark setting.

We investigate the expressive power of MATLANG, a formal language for matrix manipulation based on common matrix operations and linear algebra. The language can be extended with the operation inv for inverting a matrix. In MATLANG + inv, we can compute the transitive closure of directed graphs, whereas we show that this is not possible without inversion. Indeed, we show that the basic language can be simulated in the relational algebra with arithmetic operations, grouping, and summation. We also consider an operation eigen for diagonalizing a matrix. It is defined such that for each eigenvalue a set of mutually orthogonal eigenvectors is returned that span the eigenspace of that eigenvalue. We show that inv can be expressed in MATLANG + eigen. We put forward the open question whether there are Boolean queries about matrices, or generic queries about graphs, expressible in MATLANG + eigen but not in MATLANG + inv. Finally, the evaluation problem for MATLANG + eigen is shown to be complete for the complexity class ∃ R.

Many popular data cleaning approaches are rule-based: Constraints are formulated in a logical framework, and data is considered dirty if constraints are violated. These constraints are often discovered from data, but to ascertain their validity, user verification is necessary. Since the full set of discovered constraints is typically too large for manual inspection, recent research integrates user feedback into the discovery process. We propose a different approach that employs user interaction only at the start of the algorithm: a user manually cleans a small set of dirty tuples, and we infer the constraint underlying those repairs, called an explanation . We make use of conditional functional dependencies (CFDs) as the constraint formalism. We introduce XP lode , an on-demand algorithm which discovers the best explanation for a given repair. Guided by this explanation, data can then be cleaned using state-of-the-art CFD-based cleaning algorithms. Experiments on synthetic and real-world datasets show that the best explanation can typically be inferred using a limited number of modifications. Moreover, XP lode is substantially faster than discovering all CFDs that hold on a dataset, and is robust to noise in the modifications.

A query Q in a language L has a bounded rewriting using a set of L-definable views if there exists a query Q ′ in L such that given any dataset D , Q ( D ) can be computed by Q ′ that accesses only cached views and a small fraction D Q of D . We consider datasets D that satisfy a set of access constraints, which are a combination of simple cardinality constraints and associated indices, such that the size | D Q | of D Q and the time to identify D Q are independent of | D |, no matter how big D is. In this article, we study the problem for deciding whether a query has a bounded rewriting given a set V of views and a set A of access constraints. We establish the complexity of the problem for various query languages L , from Σ 3 p -complete for conjunctive queries (CQ) to undecidable for relational algebra (FO). We show that the intractability for CQ is rather robust even for acyclic CQ with fixed V and A , and characterize when the problem is in PTIME. To make practical use of bounded rewriting, we provide an effective syntax for FO queries that have a bounded rewriting. The syntax characterizes a key subclass of such queries without sacrificing the expressive power, and can be checked in PTIME. Finally, we investigate L 1 -to- L 2 bounded rewriting, when Q in L 1 is allowed to be rewritten into a query Q ′ in another language L 2 . We show that this relaxation does not simplify the analysis of bounded query rewriting using views.

Methods for cleaning dirty data typically rely on additional information about the data, such as user-specified constraints that specify when a database is dirty. These constraints often involve domain restrictions and illegal value combinations. Traditionally, a database is considered clean if all constraints are satisfied. However, many real-world scenario's only have a dirty database available. In such a context, we adopt a dynamic notion of data quality, in which the data is clean if an error discovery algorithm does not find any errors. We introduce forbidden itemsets which capture unlikely value co-occurrences in dirty data, and we derive properties of the lift measure to provide an efficient algorithm for mining low lift forbidden itemsets. We further introduce a repair method which guarantees that the repaired database does not contain any low lift forbidden itemsets. The algorithm uses nearest neighbor imputation to suggest possible repairs. Optional user interaction can easily be integrated into the proposed cleaning method. Evaluation on real-world data shows that errors are typically discovered with high precision, while the suggested repairs are of good quality and do not introduce new forbidden itemsets, as desired.

Large datasets introduce challenges to the scalability of query answering. Given a query Q and a dataset D, it is often prohibitively costly to compute the query answers Q(D) when D is big. To this end, one may want to use heuristics, "quick and dirty" algorithms which return approximate answers. However, in many applications it is a must to find exact query answers. So, how can we efficiently compute Q(D) when D is big or when we only have limited resources? One idea is to find a small subset D_Q of D such that Q(D_Q)=Q(D) where the size of D_Q is independent of the size of the underlying dataset D. Intuitively, when such a D_Q can be found for a query Q, the query is said to be scale independent (Armbrust et al. 2011, Armbrust et al. 2013, Fan et al. 2014). Indeed, for answering such queries the size of the underlying database does not matter, i.e., query processing is independent of the scale of the database. In this talk, I will survey various formalisms that enable large classes of queries to be scale independent. These formalisms primarily rely on the availability of access constraints, a combination of indexes and cardinality constraints, on the data (Fan et al. 15, Fan et al. 14). We will take a closer look at how, in the presence of such constraints, queries can often be compiled into efficient query plans that access a bounded amount data (Cao et al. 2014, Fan et al. 2015), and how these techniques relate to query processing in the presence of access patterns (Benedikt et al. 2015, Benedikt et al. 2014, Deutsch et al. 2007). Finally, we illustrate that scale independent queries are quite common in practice and that they indeed can be efficiently answered on big datasets when access constraints are present (Cao et al. 2015, Cao et al. 2014).

A query Q has a bounded rewriting using a set of views if there exists a query Q' expressed in the same language as Q, such that given a dataset D, Q(D) can be computed by Q' that accesses only cached views and a small fraction DQ of D. We consider datasets D that satisfy a set of access constraints, a combination of cardinality constraints and associated indices, such that the size |DQ| of DQ and the time to identify DQ are independent of |D|, no matter how big D is.

Databases in real life are often neither entirely closed-world nor entirely open-world. Databases in an enterprise are typically partially closed , in which a part of the data is constrained by master data that contains complete information about the enterprise in certain aspects. It has been shown that, despite missing tuples, such a database may turn out to have complete information for answering a query. This article studies partially closed databases from which both tuples and attribute values may be missing. We specify such a database in terms of conditional tables constrained by master data, referred to as c -instances. We first propose three models to characterize whether a c -instance T is complete for a query Q relative to master data. That is, depending on how missing values in T are instantiated, the answer to Q in T remains unchanged when new tuples are added. We then investigate three problems, to determine (a) whether a given c -instance is complete for a query Q , (b) whether there exists a c -instance that is complete for Q relative to master data available, and (c) whether a c -instance is a minimal-size database that is complete for Q . We establish matching lower and upper bounds on these problems for queries expressed in a variety of languages in each of the three models for specifying relative completeness.

This paper investigates the feasibility of querying big data by accessing a bounded amount of the data. We study boundedly evaluable queries under a form of access constraints, when their evaluation cost is determined by the queries and constraints only. While it is undecidable to determine whether FO queries are boundedly evaluable, we show that for several classes of FO queries, the bounded evaluability problem is decidable. We also provide characterization and effective syntax for their boundedly evaluable queries.

Correlation clustering is to partition a set of objects into clusters such that the number of false positives and negatives is minimised. In this paper, we combine correlation clustering and user interaction. More specifically, we allow the user to control the quality of the clustering by providing error bounds on the number of false positives and negatives. If no clusterings exist that satisfy these bounds, a set of edges is returned for user inspection such that the deletion or relabelling of these edges guarantees the existence of a clustering consistent with the error bounds. However, a user may reject the deletion or relabelling of certain edges and ask for an alternative set of edges to be provided. If no such set of edges exists, a minimal change to the error bounds should be provided, after which the interactive process continues. The focus of this paper is on the algorithmic challenges involved in returning a minimal set of edges to the user. More specifically, we formalise the Interactive Correlation Clustering problem and show that it is intractable. Therefore, we propose an approximation algorithm based on the well-known region growing technique. We experimentally validate the efficiency and accuracy of the approximation algorithm.

We address the challenging and open problem of bringing together two crucial activities in data integration and data quality, i.e., transforming data using schema mappings, and fixing conflicts and inconsistencies using data repairing. This problem is made complex by several factors. First, schema mappings and data repairing have traditionally been considered as separate activities, and research has progressed in a largely independent way in the two fields. Second, the elegant formalizations and the algorithms that have been proposed for both tasks have had mixed fortune in scaling to large databases. In the paper, we introduce a very general notion of a mapping and cleaning scenario that incorporates a wide variety of features, like, for example, user interventions. We develop a new semantics for these scenarios that represents a conservative extension of previous semantics for schema mappings and data repairing. Based on the semantics, we introduce a chase-based algorithm to compute solutions. Appropriate care is devoted to developing a scalable implementation of the chase algorithm. To the best of our knowledge, this is the first general and scalable proposal in this direction.

To make query answering feasible in big datasets, practitioners have been looking into the notion of scale independence of queries. Intuitively, such queries require only a relatively small subset of the data, whose size is determined by the query and access methods rather than the size of the dataset itself. This paper aims to formalize this notion and study its properties. We start by defining what it means to be scale-independent, and provide matching upper and lower bounds for checking scale independence, for queries in various languages, and for combined and data complexity. Since the complexity turns out to be rather high, and since scale-independent queries cannot be captured syntactically, we develop sufficient conditions for scale independence. We formulate them based on access schemas, which combine indexing and constraints together with bounds on the sizes of retrieved data sets. We then study two variations of scale-independent query answering, inspired by existing practical systems. One concerns incremental query answering: we check when query answers can be maintained in response to updates scale-independently. The other explores scale-independent query rewriting using views.

It is widely recognized that whenever different data sources need to be integrated into a single target database errors and inconsistencies may arise, so that there is a strong need to apply data-cleaning techniques to repair the data. Despite this need, database research has so far investigated mappings and data repairing essentially in isolation. Unfortunately, schema-mappings and data quality rules interact with each other, so that applying existing algorithms in a pipelined way -- i.e., first exchange then data, then repair the result -- does not lead to solutions even in simple settings. We present the Llunatic mapping and cleaning system, the first comprehensive proposal to handle schema mappings and data repairing in a uniform way. Llunatic is based on the intuition that transforming and cleaning data are different facets of the same problem, unified by their declarative nature. This holistic approach allows us to incorporate unique features into the system, such as configurable user interaction and a tunable trade-off between efficiency and quality of the solutions.

This paper introduces a new approach for conflict resolution: given a set of tuples pertaining to the same entity, it is to identify a single tuple in which each attribute has the latest and consistent value in the set. This problem is important in data integration, data cleaning and query answering. It is, however, challenging since in practice, reliable timestamps are often absent, among other things. We propose a model for conflict resolution, by specifying data currency in terms of partial currency orders and currency constraints, and by enforcing data consistency with constant conditional functional dependencies. We show that identifying data currency orders helps us repair inconsistent data, and vice versa. We investigate a number of fundamental problems associated with conflict resolution, and establish their complexity. In addition, we introduce a framework and develop algorithms for conflict resolution, by integrating data currency and consistency inferences into a single process, and by interacting with users. We experimentally verify the accuracy and efficiency of our methods using real-life and synthetic data.

We show that the evaluation of SPARQL algebra queries on various notions of annotated RDF graphs can be seen as particular cases of the evaluation of these queries on RDF graphs annotated with elements of so-called spm-semirings. Spm-semirings extend semirings, used for positive relational algebra queries on annotated relational data, with a new operator to capture the semantics of the non-monotone SPARQL operator OPTIONAL. Furthermore, spm-semiring-based annotations ensure that desired SPARQL query equivalences hold when querying annotated RDF. In addition to introducing spm-semirings, we study their properties and provide an alternative characterization of these structures in terms of semirings with an embedded boolean algebra (or seba-structure for short). This characterization allows to construct spm-semirings and to identify a universal object in the class of spm-semirings. Finally, we show that this universal object provides a concise provenance representation and can be used to evaluate SPARQL queries on arbitrary spm-semiring-annotated RDF graphs.

A query class is traditionally considered tractable if there exists a polynomial-time (PTIME) algorithm to answer its queries. When it comes to big data, however, PTIME algorithms often become infeasible in practice. A traditional and effective approach to coping with this is to preprocess data off-line, so that queries in the class can be subsequently evaluated on the data efficiently. This paper aims to provide a formal foundation for this approach in terms of computational complexity. (1) We propose a set of Π-tractable queries, denoted by ΠT Q 0 , to characterize classes of queries that can be answered in parallel poly-logarithmic time (NC) after PTIME preprocessing. (2) We show that several natural query classes are Π-tractable and are feasible on big data. (3) We also study a set ΠT Q of query classes that can be effectively converted to Π-tractable queries by refactorizing its data and queries for preprocessing. We introduce a form of NC reductions to characterize such conversions. (4) We show that a natural query class is complete for ΠT Q . (5) We also show that ΠT Q 0 ⊂ P unless P = NC, i.e., the set ΠT Q 0 of all Π-tractable queries is properly contained in the set P of all PTIME queries. Nonetheless, ΠT Q = P, i.e., all PTIME query classes can be made Π-tractable via proper refactorizations. This work is a step towards understanding the tractability of queries in the context of big data.

Data-cleaning (or data-repairing) is considered a crucial problem in many database-related tasks. It consists in making a database consistent with respect to a set of given constraints. In recent years, repairing methods have been proposed for several classes of constraints. However, these methods rely on ad hoc decisions and tend to hard-code the strategy to repair conflicting values. As a consequence, there is currently no general algorithm to solve database repairing problems that involve different kinds of constraints and different strategies to select preferred values. In this paper we develop a uniform framework to solve this problem. We propose a new semantics for repairs, and a chase-based algorithm to compute minimal solutions. We implemented the framework in a DBMS-based prototype, and we report experimental results that confirm its good scalability and superior quality in computing repairs.

Recommendation systems aim to recommend items that are likely to be of interest to users. This paper investigates several issues fundamental to such systems.

This paper investigates three problems identified in [1] for annotation propagation, namely, the view side-effect, source side-effect, and annotation placement problems. Given annotations entered for a tuple or an attribute in a view, these problems ask what tuples or attributes in the source have to be annotated to produce the view annotations. As observed in [1], these problems are fundamental not only for data provenance but also for the management of view updates. For an annotation attached to a single existing tuple in a view, it has been shown that these problems are often intractable even for views defined in terms of simple SPJU queries [1]. We revisit these problems by considering several dichotomies: (1) views defined in various subclasses of SPJU, versus SPJU views under a practical key preserving condition; (2) annotations attached to existing tuples in a view versus annotations on tuples to be inserted into the view; and (3) a single-tuple annotation versus a group of annotations. We provide a complete picture of intractability and tractability for the three problems in all these settings. We show that key preserving views often simplify the propagation analysis. Indeed, some problems become tractable for certain key preserving views, as opposed to the intractability of their counterparts that are not key preserving. However, group annotations often make the analysis harder. In addition, the problems have quite diverse complexity when annotations are attached to existing tuples in a view and when they are entered for tuples to be inserted into the view.

Data in real-life databases become obsolete rapidly. One often finds that multiple values of the same entity reside in a database. While all of these values were once correct, most of them may have become stale and inaccurate. Worse still, the values often do not carry reliable timestamps. With this comes the need for studying data currency, to identify the current value of an entity in a database and to answer queries with the current values, in the absence of reliable timestamps. This article investigates the currency of data. (1) We propose a model that specifies partial currency orders in terms of simple constraints. The model also allows us to express what values are copied from other data sources, bearing currency orders in those sources, in terms of copy functions defined on correlated attributes. (2) We study fundamental problems for data currency, to determine whether a specification is consistent, whether a value is more current than another, and whether a query answer is certain no matter how partial currency orders are completed. (3) Moreover, we identify several problems associated with copy functions, to decide whether a copy function imports sufficient current data to answer a query, whether a copy function can be extended to import necessary current data for a query while respecting the constraints, and whether it suffices to copy data of a bounded size. (4) We establish upper and lower bounds of these problems, all matching, for combined complexity and data complexity, and for a variety of query languages. We also identify special cases that warrant lower complexity.

To experimentally validate learning and approximation algorithms for XML Schema Definitions (XSDs), we need algorithms to generate uniformly at random a corpus of XSDs as well as a similarity measure to compare how close the generated XSD resembles the target schema. In this paper, we provide the formal foundation for such a testbed. We adopt similarity measures based on counting the number of common and different trees in the two languages, and we develop the necessary machinery for computing them. We use the formalism of extended DTDs (EDTDs) to represent the unranked regular tree languages. In particular, we obtain an efficient algorithm to count the number of trees up to a certain size in an unambiguous EDTD. The latter class of unambiguous EDTDs encompasses the more familiar classes of single-type, restrained competition and bottom-up deterministic EDTDs. The single-type EDTDs correspond precisely to the core of XML Schema, while the others are strictly more expressive. We also show how constraints on the shape of allowed trees can be incorporated. As we make use of a translation into a well-known formalism for combinatorial specifications, we get for free a sampling procedure to draw members of any unambiguous EDTD. When dropping the restriction to unambiguous EDTDs, i.e. taking the full class of EDTDs into account, we show that the counting problem becomes #P-complete and provide an approximation algorithm. Finally, we discuss uniform generation of single-type EDTDs, i.e., the formal abstraction of XSDs. To this end, we provide an algorithm to generate k-occurrence automata (k-OAs) uniformly at random and show how this leads to uniform generation of single-type EDTDs.

Data in real-life databases become obsolete rapidly. One often finds that multiple values of the same entity reside in a database. While all of these values were once correct, most of them may have become stale and inaccurate. Worse still, the values often do not carry reliable timestamps. With this comes the need for studying data currency, to identify the current value of an entity in a database and to answer queries with the current values, in the absence of timestamps.

This paper investigates the discovery of conditional functional dependencies (CFDs). CFDs are a recent extension of functional dependencies (FDs) by supporting patterns of semantically related constants, and can be used as rules for cleaning relational data. However, finding quality CFDs is an expensive process that involves intensive manual effort. To effectively identify data cleaning rules, we develop techniques for discovering CFDs from relations. Already hard for traditional FDs, the discovery problem is more difficult for CFDs. Indeed, mining patterns in CFDs introduces new challenges. We provide three methods for CFD discovery. The first, referred to as CFDMiner, is based on techniques for mining closed item sets, and is used to discover constant CFDs, namely, CFDs with constant patterns only. Constant CFDs are particularly important for object identification, which is essential to data cleaning and data integration. The other two algorithms are developed for discovering general CFDs. One algorithm, referred to as CTANE, is a levelwise algorithm that extends TANE, a well-known algorithm for mining FDs. The other, referred to as FastCFD, is based on the depth-first approach used in FastFD, a method for discovering FDs. It leverages closed-item-set mining to reduce the search space. As verified by our experimental study, CFDMiner can be multiple orders of magnitude faster than CTANE and FastCFD for constant CFD discovery. CTANE works well when a given relation is large, but it does not scale well with the arity of the relation. FastCFD is far more efficient than CTANE when the arity of the relation is large; better still, leveraging optimization based on closed-item-set mining, FastCFD also scales well with the size of the relation. These algorithms provide a set of cleaning-rule discovery tools for users to choose for different applications.

One of the central problems for data quality is inconsistency detection. Given a database D and a set ¿ of dependencies as data quality rules, we want to identify tuples in D that violate some rules in ¿. When D is a centralized database, there have been effective SQL-based techniques for finding violations. It is, however, far more challenging when data in D is distributed, in which inconsistency detection often necessarily requires shipping data from one site to another. This paper develops techniques for detecting violations of conditional functional dependencies (CFDs) in relations that are fragmented and distributed across different sites. (1) We formulate the detection problem in various distributed settings as optimization problems, measured by either network traffic or response time. (2)We show that it is beyond reach in practice to find optimal detection methods: the detection problem is NP-complete when the data is partitioned either horizontally or vertically, and when we aim to minimize either data shipment or response time. (3) For data that is horizontally partitioned, we provide several algorithms to find violations of a set of CFDs, leveraging the structure of CFDs to reduce data shipment or increase parallelism. (4) We verify experimentally that our algorithms are scalable on large relations and complex CFDs. (5) For data that is vertically partitioned, we provide a characterization for CFDs to be checked locally without requiring data shipment, in terms of dependency preservation. We show that it is intractable to minimally refine a partition and make it dependency preserving.

A schema-mapping is a high level specification of a data-exchange setting where a set of source-to-target dependencies is used to realize basic operations from source to target relations (such as copy, selection, join or union) while the target schema is subject to a set of target constraints (such as inclusion dependencies or key constraints). In this paper, we consider strong schema-mappings that allow for additional constraints such as source dependencies on the source schema and target-to-source dependencies from the target relations back to the source. Furthermore, strong schema-mappings may include disjunctive dependencies. We argue that this extension is desirable when the source instance is to provide both a lower and upper bound on the information that a target instance can have.

Databases in real life are often neither entirely closed-world nor entirely open-world. Indeed, databases in an enterprise are typically partially closed, in which a part of the data is constrained by master data that contains complete information about the enterprise in certain aspects [21]. It has been shown that despite missing tuples, such a database may turn out to have complete information for answering a query [9].

This article investigates the question of whether a partially closed database has complete information to answer a query. In practice an enterprise often maintains master data D m , a closed-world database. We say that a database D is partially closed if it satisfies a set V of containment constraints of the form q ( D ) ⊆ p ( D m ), where q is a query in a language L C and p is a projection query. The part of D not constrained by ( D m , V ) is open, from which some tuples may be missing. The database D is said to be complete for a query Q relative to ( D m , V ) if for all partially closed extensions D ' of D , Q ( D ') = Q ( D ), i.e., adding tuples to D either violates some constraints in V or does not change the answer to Q . We first show that the proposed model can also capture the consistency of data, in addition to its relative completeness. Indeed, integrity constraints studied for data consistency can be expressed as containment constraints. We then study two problems. One is to decide, given D m , V , a query Q in a language L Q , and a partially closed database D , whether D is complete for Q relative to ( D m , V ). The other is to determine, given D m , V and Q , whether there exists a partially closed database that is complete for Q relative to ( D m , V ). We establish matching lower and upper bounds on these problems for a variety of languages L Q and L C . We also provide characterizations for a database to be relatively complete, and for a query to allow a relatively complete database, when L Q and L C are conjunctive queries.

This paper investigates the discovery of conditional functional dependencies (CFDs). CFDs are a recent extension of functional dependencies (FDs) by supporting patterns of semantically related constants, and can be used as rules for cleaning relational data. However, finding CFDs is an expensive process that involves intensive manual effort. To effectively identify data cleaning rules, we develop techniques for discovering CFDs from sample relations. We provide three methods for CFD discovery. The first, referred to as CFDMiner, is based on techniques for mining closed itemsets, and is used to discover constant CFDs, namely, CFDs with constant patterns only. The other two algorithms are developed for discovering general CFDs. The first algorithm, referred to as CTANE, is a levelwise algorithm that extends TANE, a well-known algorithm for mining FDs. The other, referred to as FastCFD, is based on the depthfirst approach used in FastFD, a method for discovering FDs. It leverages closed-itemset mining to reduce search space. Our experimental results demonstrate the following. (a) CFDMiner can be multiple orders of magnitude faster than CTANE and FastCFD for constant CFD discovery. (b) CTANE works well when a given sample relation is large, but it does not scale well with the arity of the relation. (c) FastCFD is far more efficient than CTANE when the arity of the relation is large.

The paper investigates the question of whether a partially closed database has complete information to answer a query. In practice an enterprise often maintains master data Dm, a closed-world database. We say that a database D is partially closed if it satisfies a set V of containment constraints of the form "q(D) is a subset of p(Dm)", where q is a query in a language Lc and p is a projection query. The part of D not constrained by (Dm,V) is open, from which some tuples may be missing. The database D is said to be complete for a query Q relative to (Dm,V) if for all partially closed extensions D' of D, Q(D')=Q(D), i.e., adding tuples to D either violates some constraints in V or does not change the answer to Q.

The paper proposes an extension of CFDs [1], referred to as extended Conditional Functional Dependencies (eCFDs). In contrast to CFDs, eCFDs specify patterns of semantically related values in terms of disjunction and inequality, and are capable of catching inconsistencies that arise in practice but cannot be detected by CFDs. The increase in expressive power does not incur extra complexity: we show that the satisfiability and implication analyses of eCFDs remain NP - complete and coNP -complete, respectively, the same as their CFDs counterparts. In light of the intractability, we present an algorithm that approximates the maximum number of eCFDs that are satisfiable. In addition, we revise SQL techniques for detecting CFD violations, and show that violations of multiple eCFDs can be captured via a single pair of SQL queries. We also introduce an incremental SQL technique for detecting eCFD violations in response to database updates. We experimentally verify the effectiveness and efficiency of our SQL -based detection methods.

The paper investigates fundamental decision problems and composition synthesis for Web services commonly found in practice. We propose a notion of synthesized Web services (ASTs) to specify the behaviors of the services. Upon receiving a sequence of input messages, an AST issues multiple queries to a database and generates actions, in parallel; it produces external messages and database updates by synthesizing the actions parallelly generated. In contrast to previous models for Web services, ASTs advocate parallel processing and (deterministic) synthesis of actions. We classify ASTs based on what queries an AST can issue, how the synthesis of actions is expressed, and whether unbounded input sequences are allowed in a single interaction session. We show that the behaviors of Web services supported by various prior models, data-driven or not, can be specified by different AST classes. For each of these classes we study the non-emptiness, validation and equivalence problems, and establish matching upper and lower bounds on these problems. We also provide complexity bounds on composition synthesis for these AST classes, identifying decidable cases.

We present Semandaq, a prototype system for improving the quality of relational data. Based on the recently proposed conditional functional dependencies (CFDs), it detects and repairs errors and inconsistencies that emerge as violations of these constraints. We demonstrate the following functionalities supported by Semandaq: (a) an interface for specifying CFDs; (b) a visual tool for automated detection of CFD violations in relational data, leveraging efficient SQL-based techniques; (c) extensive visual data exploration capabilities that provide the user with various measures of the quality of the data; (d) repair (cleaning) functionality without excess human interaction, built upon CFD-based cleaning algorithms; we show how Semandaq allows for a natural exploration of the quality of the obtained repairs. Semandaq is a promising tool that provides easy access and user-friendly data quality facilities for any relational database system.

Integrity constraints, a.k.a . data dependencies, are being widely used for improving the quality of schema . Recently constraints have enjoyed a revival for improving the quality of data . The tutorial aims to provide an overview of recent advances in constraint-based data cleaning.

We propose a class of integrity constraints for relational databases, referred to as conditional functional dependencies (CFDs), and study their applications in data cleaning. In contrast to traditional functional dependencies (FDs) that were developed mainly for schema design, CFDs aim at capturing the consistency of data by enforcing bindings of semantically related values. For static analysis of CFDs we investigate the consistency problem , which is to determine whether or not there exists a nonempty database satisfying a given set of CFDs, and the implication problem , which is to decide whether or not a set of CFDs entails another CFD. We show that while any set of transitional FDs is trivially consistent, the consistency problem is NP-complete for CFDs, but it is in PTIME when either the database schema is predefined or no attributes involved in the CFDs have a finite domain. For the implication analysis of CFDs, we provide an inference system analogous to Armstrong's axioms for FDs, and show that the implication problem is coNP-complete for CFDs in contrast to the linear-time complexity for their traditional counterpart. We also present an algorithm for computing a minimal cover of a set of CFDs. Since CFDs allow data bindings, in some cases CFDs may be physically large, complicating the detection of constraint violations. We develop techniques for detecting CFD violations in SQL as well as novel techniques for checking multiple constraints by a single query. We also provide incremental methods for checking CFDs in response to changes to the database. We experimentally verify the effectiveness of our CFD-based methods for inconsistency detection. This work not only yields a constraint theory for CFDs but is also a step toward a practical constraint-based method for improving data quality.

A number of languages have been developed for specifying XML publishing, that is, transformations of relational data into XML trees. These languages generally describe the behaviors of a middleware controller that builds an output tree iteratively, issuing queries to a relational source and expanding the tree with the query results at each step. To study the complexity and expressive power of XML publishing languages, this article proposes a notion of publishing transducers , which generate XML trees from relational data. We study a variety of publishing transducers based on what relational queries a transducer can issue, what temporary stores a transducer can use during tree generation, and whether or not some tree nodes are allowed to be virtual, that is, excluded from the output tree. We first show how existing XML publishing languages can be characterized by such transducers, and thus provide a synergy between theory and practice. We then study the membership, emptiness, and equivalence problems for various classes of transducers. We establish lower and upper bounds, all matching, ranging from PTIME to undecidable. Finally, we investigate the expressive power of these transducers and existing languages. We show that when treated as relational query languages, different classes of transducers capture either complexity classes (e.g., PSPACE) or fragments of datalog (e.g., linear datalog). For tree generation, we establish connections between publishing transducers and logical transductions, among other things.

We propose a class of constraints, referred to as conditional functional dependencies (CFDs), and study their applications in data cleaning. In contrast to traditional functional dependencies (FDs) that were developed mainly for schema design, CFDs aim at capturing the consistency of data by incorporating bindings of semantic ally related values. For CFDs we provide an inference system analogous to Armstrong's axioms for FDs, as well as consistency analysis. Since CFDs allow data bindings, a large number of individual constraints may hold on a table, complicating detection of constraint violations. We develop techniques for detecting CFD violations in SQL as well as novel techniques for checking multiple constraints in a single query. We experimentally evaluate the performance of our CFD-based methods for inconsistency detection. This not only yields a constraint theory for CFDs but is also a step toward a practical constraint-based method for improving data quality.

We study the problem of answering queries posed on virtual views of XML documents, a problem commonly encountered when enforcing XML access control and integrating data. We approach the problem by rewriting queries on views into equivalent queries on the underlying document, and thus avoid the overhead of view materialization and maintenance. We consider possibly recursively defined XML views and study the rewriting of both XPath and regular XPath queries. We show that while rewriting is not always possible for XPath over recursive views, it is for regular XPath; however, the rewritten query may be of exponential size. To avoid this prohibitive cost we propose a rewriting algorithm that characterizes rewritten queries as a new form of automata, and an efficient algorithm to evaluate the automaton-represented queries. These allow us to answer queries on views in linear time. We have fully implemented a prototype system, SMOQE, which yields the first regular XPath engine and a practical solution for answering queries over possibly recursively defined XML views.

A number of languages have been developed for specifying XML publishing, i.e., transformations of relational data into XML trees. These languages generally describe the behaviors of a middleware controller that builds an output tree iteratively, issuing queries to a relational source and expanding the tree with the query results at each step. To study the complexity and expressive power of XML publishing languages, this paper proposes a notion of publishing transducers. Unlike automata for querying XML data, a publishing transducer generates a new XML tree rather than performing a query on an existing tree. We study a variety of publishing transducers based on what relational queries a transducer can issue, what temporary stores a transducer can use during tree generation, and whether or not some tree nodes are allowed to be virtual, i.e., excluded from the output tree. We first show how existing XML publishing languages can be characterized by such transducers. We then study the members ip, emptiness and equivalence problems for various classes of transducers and existing publishing languages. We establish lower and upper bounds, all matching except one, ranging from PTIME to undecidable. Finally, we investigate the expressive power of these transducers and existing languages. We show that when treated as relational query languages, different classes of transducers capture either complexity classes (e.g., PSPACE) or fragments of datalog (e.g., linear datalog). For tree generation, we establish connections between publishing transducers and logical transductions.

This paper revisits the analysis of annotation propagation from source databases to views defined in terms of conjunctive (SPJ) queries. Given a source database D, an SPJ query Q, the view Q(D) and a tuple ΔV in the view, the view (resp. source) side-effect problem is to find a minimal set ΔD of tuples such that the deletion of ΔD from D results in the deletion of ΔV from Q(D) while minimizing the side effects on the view (resp. the source). A third problem, referred to as the annotation placement problem, is to find a single base tuple ΔD such that annotation in a field of ΔD propagates to ΔV while minimizing the propagation to other fields in the view Q(D). These are important for data provenance and the management of view updates. However important, these problems are unfortunately NP-hard for most subclasses of SPJ views [5].To make the annotation propagation analysis feasible in practice, we propose a key preserving condition on SPJ views, which requires that the projection fields of an SPJ view Q retain a key of each base relation involved in Q. While this condition is less restrictive than other proposals [11, 14], it often simplifies the annotation propagation analysis. Indeed, for key-preserving SPJ views the annotation placement problem coincides with the view side-effect problem, and the view and source side-effect problems become tractable. In addition we generalize the setting of [5] by allowing ΔV to be a group of tuples to be deleted, and investigate the insertion of tuples to the view. We show that group updates make the analysis harder: these problems become NP-hard for several subclasses of SPJ views. We also show that for SPJ views the source and view side-effect problems are NP-hard for single-tuple insertion, but are tractable for some subclasses of SPJ for group insertions, in the presence or in the absence of the key preservation condition.

We give two efficient on-line algorithms to simplify weighted graphs by eliminating degree-two vertices. Our algorithms are on-line---they react to updates on the data, keeping the simplification up-to-date. We provide both analytical and empirical evaluations of the efficiency of our algorithms. We prove an O(log n) upper bound on the amortized time complexity of our maintenance algorithms, with n the number of insertions. One of our algorithms can handle in logarithmic time the deletions of vertices and edges as well.

Annotations play a central role in the curation of scientific databases. Despite their importance, data formats and schemas are not designed to manage the increasing variety of annotations. Moreover, DBMS’s often lack support for storing and querying annotations. Furthermore, annotations and data are only loosely coupled. This paper introduces an annotation-oriented data model for the manipulation and querying of both data and annotations. In particular, the model allows for the specification of annotations on sets of values and for effectively querying the information on their association. We use the concept of block to represent an annotated set of values. Different colors applied to the blocks represent different annotations. We introduce a color query language for our model and prove it to be both complete (it can express all possible queries over the class of annotated databases), and minimal (all the algebra operators are primitive). We present MONDRIAN, a prototype implementation of our annotation mechanism, and we conduct experiments that investigate the set of parameters which influence the evaluation cost for color queries.

We study the satisfiability problem associated with XPath in the presence of DTDs. This is the problem of determining, given a query p in an XPath fragment and a DTD D, whether or not there exists an XML document T such that T conforms to D and the answer of p on T is nonempty. We consider a variety of XPath fragments widely used in practice, and investigate the impact of different XPath operators on satisfiability analysis. We first study the problem for negation-free XPath fragments with and without upward axes, recursion and data-value joins, identifying which factors lead to tractability and which to NP-completeness. We then turn to fragments with negation but without data values, establishing lower and upper bounds in the absence and in the presence of upward modalities and recursion. We show that with negation the complexity ranges from PSPACE to EXPTIME. Moreover, when both data values and negation are in place, we find that the complexity ranges from NEXPTIME to undecidable. Finally, we give a finer analysis of the problem for particular classes of DTDs, exploring the impact of various DTD constructs, identifying tractable cases, as well as providing the complexity in the query size alone.

In the context of mining for frequent patterns using the standard levelwise algorithm, the following question arises: given the current level and the current set of frequent patterns, what is the maximal number of candidate patterns that can be generated on the next level? We answer this question by providing tight upper bounds, derived from a combinatorial result from the sixties by Kruskal and Katona. Our result is useful to secure existing algorithms from a combinatorial explosion of the number of candidate patterns.

In the context of mining for frequent patterns using the standard level-wise algorithm, the following question arises: given the current level and the current set of frequent patterns, what is the maximal number of candidate patterns that can be generated on the next level? We answer this question by providing a tight upper bound, derived from a combinatorial result by J. Kruskal (1963) and G. Katona (1968). Our result is useful for reducing the number of database scans.

We consider spatial databases in the plane that can be defined by polynomial constraint formulas. Motivated by applications in geographic information systems, we investigate linear approximations of spatial databases and study in which language they can be expressed effectively. Specifically, we show that they cannot be expressed in the standard first-order query language for polynomial constraint databases but that an extension of this first-order language with transitive closure suffices to express the approximation query in an effective manner. Furthermore, we introduce an extension of transitive-closure logic and show that this logic is complete for the computable queries on linear spatial databases. This result together with our first result implies that this extension of transitive-closure logic can express all computable topological queries on arbitrary spatial databases in the plane.