ACM Transactions on

Database Systems (TODS)

Latest Articles

ChronicleDB: A High-Performance Event Store

Reactive security monitoring, self-driving cars, the Internet of Things (IoT), and many other novel applications require systems for both writing events arriving at very high and fluctuating rates to persistent storage as well as supporting analytical ad hoc queries. As standard database systems are not capable of delivering the required write... (more)

On the Expressive Power of Query Languages for Matrices

We investigate the expressive power of MATLANG, a formal language for matrix manipulation based on common matrix operations and linear algebra. The... (more)

Dichotomies for Evaluating Simple Regular Path Queries

Regular path queries (RPQs) are a central component of graph databases. We investigate decision and enumeration problems concerning the evaluation of... (more)

Efficient Algorithms for Approximate Single-Source Personalized PageRank Queries

Given a graph G, a source node s, and a target node t, the personalized PageRank (PPR) of t with respect to s is the probability that a random walk... (more)


Updates to the TODS Editorial Board

As of January 1, 2018, five Associate Editors—Walid Aref, Graham Cormode, Gautam Das, Sabrina De Capitani di Vimercati, and Dirk Van Gucht—ended their terms, each having served on the editorial board for six years. Walid, Graham, Gautam, Sabrina, and Dirk have provided very substantial, high-caliber service to the journal and the database community. Also five new Associate Editors have joined the editorial board: Angela Bonifati, Université Claude Bernard Lyon 1, Wolfgang Lehner, TU Dresden, Dan Olteanu, University of Oxford, Evaggelia Pitoura, University of Ioannina, and Bernhard Seeger, University of Marburg. All five are highly regarded scholars in database systems.

Please read more here.

Updates to the TODS Editorial Board

As of January 1, 2017, three Associate Editors, Paolo Ciaccia, Divyakant Agrawal, and Sihem Amer-Yahia, ended their terms, each having served on the editorial board for some six years. In addition, they will stay on until they complete their current loads. We are fortunate that they have donated their time and world-class expertise during these years. Also three new Associate Editors have joined the editorial board: Feifei Li, University of Utah, Kian-Lee Tan, National University of Singapore, and Jeffrey Xu Yu, Chinese University of Hong Kong. All three are highly regarded scholars in database systems. 

Please read more here.

Forthcoming Articles
Computing Optimal Repairs for Functional Dependencies

We investigate the complexity of computing an optimal repair of an inconsistent database, in the case where integrity constraints are Functional Dependencies (FDs). We focus on two types of repairs: an optimal subset repair (optimal S-repair) that is obtained by a minimum number of tuple deletions, and an optimal update repair (optimal U-repair) that is obtained by a minimum number of value (cell) updates. For computing an optimal S-repair, we present a polynomial-time algorithm that succeeds on certain sets of FDs and fails on others. We prove the following about the algorithm. When it succeeds, it can also incorporate weighted tuples and duplicate tuples. When it fails, the problem is NP-hard, and in fact, APX-complete (hence, cannot be approximated better than some constant). Thus, we establish a dichotomy in the complexity of computing an optimal S-repair. We present general analysis techniques for the complexity of computing an optimal U-repair, some based on the dichotomy for S-repairs. We also draw a connection to a past dichotomy in the complexity of finding a "most probable database" that satisfies a set of FDs with a single attribute on the left hand side; the case of general FDs was left open, and we show how our dichotomy provides the missing generalization and thereby settles the open problem.

Design and Evaluation of an RDMA-aware Data Shuffling Operator for Parallel Database Systems

The commoditization of high-performance networking has sparked research interest in the RDMA capability of this hardware. One-sided RDMA primitives, in particular, have generated substantial excitement due to the ability to directly access remote memory from within an application without involving the TCP/IP stack or the remote CPU. This paper considers how to leverage RDMA to improve the analytical performance of parallel database systems. To shuffle data efficiently using RDMA, one needs to consider a complex design space that includes (1) the number of open connections, (2) the contention for the shared network interface, (3) the RDMA transport function, and (4) how much memory should be reserved to exchange data between nodes during query processing. We contribute eight designs that capture salient trade-offs in this design space as well as an adaptive algorithm to dynamically manage RDMA-registered memory. We comprehensively evaluate how transport-layer decisions impact the query performance of a database system for different generations of InfiniBand. We find that a shuffling operator that uses the RDMA Send/Receive transport function over the Unreliable Datagram transport service can transmit data up to 4× faster than an RDMA-capable MPI implementation in a 16-node cluster. The response time of TPC-H queries improves by as much as 2×.

General Temporally-Biased Sampling Schemes for Online Model Management

To maintain the accuracy of supervised learning models in the presence of evolving data streams, we provide temporally-biased sampling schemes that weight recent data most heavily, with inclusion probabilities for a given data item decaying over time according to a specified ``decay function'. We then periodically retrain the models on the current sample. This approach speeds up the training process relative to training on all of the data. Moreover, time-biasing lets the models adapt to recent changes in the data while---unlike in a sliding-window approach---still keeping some old data to ensure robustness in the face of temporary fluctuations and periodicities in the data values. In addition, the sampling-based approach allows existing analytic algorithms for static data to be applied to dynamic streaming data essentially without change. We provide and analyze both a simple sampling scheme (T-TBS) that probabilistically maintains a target sample size and a novel reservoir-based scheme (R-TBS) that is the first to provide both control over the decay rate and a guaranteed upper bound on the sample size. If the decay function is exponential, then control over the decay rate is complete, and R-TBS maximizes both expected sample size and sample-size stability. For general decay functions, the actual item inclusion probabilities can be made arbitrarily close to the nominal probabilities, and we provide a scheme that allows a trade-off between sample footprint and sample-size stability. R-TBS rests on the notion of a ``fractional sample' and allows for data arrival rates that are unknown and time varying (unlike T-TBS). The R-TBS and T-TBS schemes are of independent interest, extending the known set of unequal-probability sampling schemes. We discuss distributed implementation strategies; experiments in Spark illuminate the performance and scalability of the algorithms, and show that our approach can increase machine learning robustness in the face of evolving data.

On the Language of Nested Tuple Generating Dependencies

During the past decade, schema mappings have been extensively used in formalizing and studying such critical data interoperability tasks as data exchange and data integration. Much of the work has focused on GLAV mappings, i.e., schema mappings specified by source-to-target tuple-generating dependencies (s-t tgds), and on schema mappings specified by second-order tgds (SO tgds), which constitute the closure of GLAV mappings under composition. In addition, nested GLAV mappings have also been considered, i.e., schema mappings specified by nested tgds, which have expressive power intermediate between s-t tgds and SO tgds. Even though nested GLAV mappings have been used in data exchange systems, such as IBM?s Clio, no systematic investigation of this class of schema mappings has been carried out so far. In this paper, we embark on such an investigation by focusing on the basic reasoning tasks, algorithmic problems, and structural properties of nested GLAV mappings. One of our main results is the decidability of the implication problem for nested tgds. We also analyze the structure of the core of universal solutions with respect to nested GLAV mappings and develop useful tools for telling apart SO tgds from nested tgds. By discovering deeper structural properties of nested GLAV mappings, we show that also the following problem is decidable: given a nested GLAV mapping, is it logically equivalent to a GLAV mapping?

Efficient enumeration algorithms for regular document spanners

Regular expressions and automata models with capture variables are core tools in rule-based information extraction. These formalisms, also called regular document spanners, use regular languages in order to locate the data that a user wants to extract from a text document, and then store this data into variables. Since document spanners can easily generate large outputs, it is important to have good evaluation algorithms that can generate the extracted data in a quick succession, and with relatively little precomputation time. Towards this goal, we present a practical evaluation algorithm that allows constant delay enumeration of a spanner's output after a precomputation phase that is linear in the document. While the algorithm assumes that the spanner is specified in a syntactic variant of variable set automata, we also study how it can be applied when the spanner is specified by general variable set automata, regex formulas, or spanner algebras. Finally, we study the related problem of counting the number of outputs of a document spanner, providing a fine grained analysis of the classes of document spanners that support efficient enumeration of their results.

EKTELO: A Framework for Defining Differentially-Private Computations

The adoption of differential privacy is growing but the complexity of designing private, efficient and accurate algorithms is still high. We propose a novel programming framework and system, ?ktelo, for implementing both existing and new privacy algorithms. For the task of answering linear counting queries, we show that nearly all existing algorithms can be composed from operators, each conforming to one of a small number of operator classes. While past programming frameworks have helped to ensure the privacy of programs, the novelty of our framework is its significant support for authoring accurate and efficient (as well as private) programs. After describing the design and architecture of the ?ktelo system, we show that ?ktelo is expressive, allows for safer implementations through code reuse, and that it allows both privacy novices and experts to easily design algorithms. We provide a number of novel implementation techniques to support the generality and scalability of ?ktelo operators. These include methods to automatically compute lossless reductions of the data representation, implicit matrices that avoid materialized state but still support computations, and iterative inference implementations which generalize techniques from the privacy literature. We demonstrate the utility of ?ktelo by designing several new state-of-the-art algorithms, most of which result from simple re-combinations of operators defined in the framework. We study the accuracy and scalability of ?ktelo plans in a thorough empirical evaluation.

A Game-theoretic Approach to Data Interaction

As most users do not precisely know the structure and/or the content of databases, their queries do not exactly reflect their information needs. The database management systems (DBMS) may interact with users and use their feedback on the returned results to learn the information needs behind their queries. Current query interfaces assume that users do not learn and modify the way way they express their information needs in form of queries during their interaction with the DBMS. Using a real-world interaction workload, we show that users learn and modify how to express their information needs during their interactions with the DBMS and their learning is accurately modeled by a well-known reinforcement learning mechanism. As current data interaction systems assume that users do not modify their strategies, they cannot discover the information needs behind users' queries effectively. We model the interaction between users and DBMS as a game with identical interest between two rational agents whose goal is to establish a common language for representing information needs in form of queries. We propose a reinforcement learning method that learns and answers the information needs behind queries and adapts to the changes in users' strategies and prove that it improves the effectiveness of answering queries stochastically speaking. We propose two efficient implementation of this method over large relational databases. Our extensive empirical studies over real-world query workloads indicate that our algorithms are efficient and effective.

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