Principles Of Database Systems Research Articles

Database Theory Column

The 42nd edition of the ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS) was held from June 19 to June 21, 2023, in Seattle, USA. After two years of online-only and a year of hybrid conferences, PODS 2023 went back to a fully in-person conference format. As is customary, the symposium was held jointly with the ACM International Conference on Management of Data (SIGMOD).

Database Theory Column Report on PODS 2021

The 40th edition of the ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS) was held from June 20 to June 25, 2021, in Xi'an, Shaanxi, China. Due to the COVID-19 pandemic, it was organized in hybrid mode, with a local event primarily targeting the Chinese data management community and as a virtual (on- line) conference for the international community. As in previous years, the symposium was held jointly with the ACM International Conference on Management of Data (SIGMOD). PODS focuses on theoretical aspects of data management systems, and the co-location with SIGMOD stimulates interaction between theory-oriented and system-oriented research.

Database Theory Column

The 39th edition of the ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS) was planned to take place from June 14 to June 19, 2020, in Portland, OR, USA. Due to the coronavirus pandemic, it was organized instead as a virtual (online) conference on the same dates. As in previous years, the symposium was held jointly with the ACM International Conference on Management of Data (SIGMOD). PODS focuses on the theoretical aspects of data management systems, and the co-location with SIGMOD stimulates interaction between theory-oriented and system-oriented research.

Database Theory Column Report on PODS 2019

The 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS 2019) took place in Amsterdam, The Netherlands, from July 1 to July 3, 2019, in conjunction with the 2019 ACM SIGMOD International Conference on Management of Data. PODS focuses on theoretical aspects of data management, and the co-location with SIGMOD stimulates interaction between theory-oriented and systems-oriented research. Since the first edition of the symposium in 1982, the PODS papers are distinguished by a rigorous approach to widely diverse problems in data management, often bringing to bear techniques from a variety of different areas, including computational logic, finite model theory, computational complexity, algorithm design and analysis, programming languages, and artificial intelligence.

Database Theory Column Report on PODS 2018

The 37th edition of the ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS) took place from June 11 to June 13, 2018, in Houston, Texas, USA. As in previous years, the symposium was held jointly with the ACM SIGMOD International Conference on Management of Data (SIGMOD). PODS focuses on theoretical aspects of data management systems and techniques, and the co-location with SIGMOD stimulates interaction between theory-oriented and system-oriented research.

Untangling Blockchain: A Data Processing View of Blockchain Systems

Blockchain technologies are gaining massive momentum in the last few years. Blockchains are distributed ledgers that enable parties who do not fully trust each other to maintain a set of global states. The parties agree on the existence, values, and histories of the states. As the technology landscape is expanding rapidly, it is both important and challenging to have a firm grasp of what the core technologies have to offer, especially with respect to their data processing capabilities. In this paper, we first survey the state of the art, focusing on private blockchains (in which parties are authenticated). We analyze both in-production and research systems in four dimensions: distributed ledger, cryptography, consensus protocol, and smart contract. We then present BLOCKBENCH, a benchmarking framework for understanding performance of private blockchains against data processing workloads. We conduct a comprehensive evaluation of three major blockchain systems based on BLOCKBENCH, namely Ethereum, Parity, and Hyperledger Fabric. The results demonstrate several trade-offs in the design space, as well as big performance gaps between blockchain and database systems. Drawing from design principles of database systems, we discuss several research directions for bringing blockchain performance closer to the realm of databases.

A new memetic algorithm for multiple graph alignment

A new memetic algorithm for multiple graph alignment

Single Pass Spectral Sparsification in Dynamic Streams

We present the first single pass algorithm for computing spectral sparsifiers for graphs in the dynamic semi-streaming model. Given a single pass over a stream containing insertions and deletions of edges to a graph $G$, our algorithm maintains a randomized linear sketch of the incidence matrix of $G$ into dimension $O (\frac{1}{\epsilon^2}n{polylog} (n))$. Using this sketch, at any point, the algorithm can output a $(1 \pm \epsilon)$ spectral sparsifier for $G$ with high probability. While $O (\frac{1}{\epsilon^2} n{polylog}(n))$ space algorithms are known for computing cut sparsifiers in dynamic streams [K. J. Ahn, S. Guha, and A. McGregor, in Proceedings of the 31st ACM Symposium on Principles of Database Systems, 2012, pp. 5--14; A. Goel, M. Kapralov, and I. Post, \hrefhttp://arXiv.org/abs/1203.4900 arXiv:1203.4900, 2002] and spectral sparsifiers in insertion-only streams [J. A. Kelner and A. Levin, Theory Comput. Syst., 53 (2013), pp. 243--262], prior to our work, the best known single pass algorithm for maintaining spectral sparsifiers in dynamic streams required sketches of dimension $\Omega (\frac{1}{\epsilon^2}n^{5/3})$ [K. J. Ahn, S. Guha, and A. McGregor, in Proceedings of the 16th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, 2013, pp. 1--10]. To achieve our result, we show that using a coarse sparsifier for $G$ and a linear sketch of $G$'s incidence matrix, it is possible to sample edges by effective resistance, obtaining a spectral sparsifier of arbitrary precision. Sampling from the sketch requires a novel application of $\ell_2/\ell_2$ sparse recovery, a natural extension of the $\ell_0$ methods used for cut sparsifiers in [K. J. Ahn, S. Guha, and A. McGregor, in Proceedings of the 31st ACM Symposium on Principles of Database Systems, 2012, pp. 5--14]. Recent work on row sampling for matrix approximation gives a recursive approach for obtaining the required coarse sparsifiers [G. L. Miller and R. Peng, ŭlhttp://arXiv.org/abs/1211.2713v1, 2012]. Under certain restrictions, our approach also extends to the problem of maintaining a spectral approximation for a general matrix $A^\top A$ given a stream of updates to rows in $A$.

Session details: Principles of database systems

Session details: Principles of database systems

Better Size Estimation for Sparse Matrix Products

We consider the problem of doing fast and reliable estimation of the number z of non-zero entries in a sparse boolean matrix product. This problem has applications in databases and computer algebra.Let n denote the total number of non-zero entries in the input matrices. We show how to compute a 1±ε approximation of z (with small probability of error) in expected time for any \(\varepsilon> 4/\sqrt[4]{z}\). The previously best estimation algorithm, due to Cohen (J. Comput. Syst. Sci. 53(3):441–453, 1997), uses time . We also present a variant using I/Os in expectation in the cache-oblivious model.In contrast to these results, the currently best algorithms for computing a sparse boolean matrix product use time ω(n 4/3) (resp. ω(n 4/3/B) I/Os), even if the result matrix is restricted to nonzero entries.Our algorithm combines the size estimation technique of Bar-Yossef et al. (Proceedings of the 6th International Workshop on Randomization and Approximation Techniques (RANDOM ’02), pp. 1–10, 2002) with a particular class of pairwise independent hash functions that allows the sketch of a set of the form to be computed in expected time and I/Os.We then describe how sampling can be used to maintain (independent) sketches of matrices that allow estimation to be performed in time o(n) if z is sufficiently large. This gives a simpler alternative to the sketching technique of Ganguly et al. (Proceedings of the 24th ACM Symposium on Principles of Database Systems (PODS ’05), pp. 259–270, 2005), and matches a space lower bound shown in that paper.Finally, we present experiments on real-world data sets that show the accuracy of both our methods to be significantly better than the worst-case analysis predicts.

Session details: Principles of database systems

Session details: Principles of database systems

Ranking-aware integration and explorative search of distributed bio-data

Motivation and Objectives High-throughput production of both biomolecular data and their annotations is providing a rapidly increasing amount of very valuable information that can potentially help finding also long-searched answers to fundamental biomedical questions. Yet, such data deluge makes difficult to extract the information most reliable and most related to the increasingly complex biomedical questions to be answered, which can simultaneously regard many heterogeneous aspects of single or multiple organisms, biological tissues, cells or biomolecular entities. To address such complex questions, many bio-data about several heterogeneous topics, which are available but dispersed in different data sources, must be searched, extracted, integrated and comprehensively queried. Different approaches have been proposed to combine individual search services available on the Web in order to support such heterogeneous searches (Hull et al., 2006; Nekrutenko, 2010). Yet, they rarely rely on a general model of the services to be integrated and none considers, in the integration process, the often available partial rankings of the data to be integrated. Lately, Search Computing (Ceri et al., 2010) has been proposed as a new software framework to build answers to complex search queries by interacting with a collection of cooperating search services and using ranking and joining of results as the dominant factors for service composition. By leveraging the peculiar features of search services, it offers query approaches, execution plans, plan optimization techniques, query configuration tools, and exploratory user interfaces. Here, we report and discuss our work aimed at supporting the explorative search of heterogeneous distributed bio-data and the automatic integration and global ranking of their individual search results, also taking into account the partial rankings of individual searches. In so doing, we make a step towards the computational support required for complex biomedical question answering and biomedical knowledge discovery. Methods According to the Service Mart modeling approach of Search Computing (Ceri et al., 2010), we selected an initial set of typical biomolecular topics (i.e. Protein, Gene, Gene Expression and Biological Function) and modeled the Service Marts (i.e. the generalized and normalized conceptual description) of the bioinformatics services that provide data regarding such topics. We did so by identifying their main and common attributes and normalizing their names. We also defined the semantic Connection Patterns, i.e. the pair-wise coupling, between Service Marts of services that provide data about different topics. This was done by identifying pairs of normalized attributes of the connected Service Marts and defining their comparison predicates, as conjunctive Boolean expressions, that allow joining their values semantically. In so doing, we defined the Semantic Resource Framework (SRF) depicted in Figure 1, which constitutes the reference used by Search Computing to enable the exploration of the services registered in the framework and integrate the data that they provide (Ceri et al., 2010). Then, using available Search Computing tools, we registered in the Search Computing framework five bioinformatics search services that provide data about the topics and semantic associations described in the biomolecular

Session details: Principles of database systems

Session details: Principles of database systems

Session details: Principles of database systems

Session details: Principles of database systems

Session details: Principles of database systems

Session details: Principles of database systems

Invited article foreword

In this issue, the Invited Articles section is comprised of the article “Continuous Sampling from Distributed Streams” by Graham Cormode, Muthu Muthukrishnan, Ke Yi and Qin Zhang. This article was selected from the 29th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, held in Indianapolis, Indiana, June 7--9, 2010. I thank the Program Committee of PODS 2010 and the PC Chair, Dirk Van Gucht, for their help in selecting this invited article. I am also grateful to JACM Associate Editor Phokion Kolaitis for his editorial work on this article.

Invited Article Foreword

No abstract available.

Strong Lower Bounds for Approximating Distribution Support Size and the Distinct Elements Problem

We consider the problem of approximating the support size of a distribution from a small number of samples, when each element in the distribution appears with probability at least $\frac{1}{n}$. This problem is closely related to the problem of approximating the number of distinct elements in a sequence of length $n$. Charikar, Chaudhuri, Motwani, and Narasayya [in Proceedings of the Nineteenth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, 2000, pp. 268-279] and Bar-Yossef, Kumar, and Sivakumar [in Proceedings of the Thirty-Third Annual ACM Symposium on Theory of Computing, ACM Press, New York, 2001, pp. 266-275] proved that multiplicative approximation for these problems within a factor $\alpha>1$ requires $\Theta(\frac{n}{\alpha^2})$ queries to the input sequence. Their lower bound applies only when the number of distinct elements (or the support size of a distribution) is very small. For both problems, we prove a nearly linear in $n$ lower bound on the query complexity, applicable even when the number of distinct elements is large (up to linear in $n$) and even for approximation with additive error. At the heart of the lower bound is a construction of two positive integer random variables, $\mathsf{X}_1$ and $\mathsf{X}_2$, with very different expectations and the following condition on the first $k$ moments: $\mathsf{E}[\mathsf{X}_1]/\mathsf{E}[\mathsf{X}_2] = \mathsf{E}[\mathsf{X}_1^2]/\mathsf{E}[\mathsf{X}_2^2] = \cdots = \mathsf{E}[\mathsf{X}_1^k]/\E[\mathsf{X}_2^k]$. It is related to a well-studied mathematical question, the truncated Hamburger problem, but differs in the requirement that our random variables have to be supported on integers. Our lower bound method is also applicable to other problems and, in particular, gives a new lower bound for the sample complexity of approximating the entropy of a distribution.

Tree-Related Widths of Graphs and Hypergraphs

A hypergraph pair is a pair $(G,H)$ where G and H are hypergraphs on the same set of vertices. We extend the definitions of hypertree-width [G. Gottlob, N. Leone, and F. Scarcello, J. Comput. System Sci., 64 (2002), pp. 579–627] and generalized hypertree-width [G. Gottlob, N. Leone, and F. Scarcello, J. Comput. System Sci., 66 (2003), pp. 775–808] from hypergraphs to hypergraph pairs. We show that for constant k the problem of deciding whether a hypergraph pair has generalized hypertree-width $\leq k$, is equivalent to the hypergraph sandwich problem (HSP) [A. Lustig and O. Shmueli, J. Algorithms, 30 (1999), pp. 400–422]. It was recently proved in [G. Gottlob, Z. Miklós, and Th. Schwentick, Proceedings of the Symposium on Principles of Database Systems $(PODS\/07)$] that the HSP is NP-complete. For constant k there is a polynomial time algorithm that decides whether a given hypergraph pair has hypertree-width $\leq k$. (For hypertree-width of hypergraphs, this was shown in [G. Gottlob, N. Leone, and F. Scarcello, J. Comput. System Sci., 64 (2002), pp. 579–627].) It follows that the HSP is solvable in polynomial time for inputs $(G,H)$ satisfying: $\operatorname{ghw}(G,H)\leq 1$ if and only if $\operatorname{hw}(G,H)\leq 1$. Besides this practical interest, hypergraph pairs serve as a tool for giving a common proof for the game theoretic characterizations of tree-width [P. D. Seymour and R. Thomas, J. Combin. Theory Ser. B, 58 (1993), pp. 22–33] and hypertree-width [G. Gottlob, N. Leone, and F. Scarcello, J. Comput. System Sci., 66 (2003), pp. 775–808]. Furthermore, they enable us to show a compactness property of generalized hypertree-width for a large class of hypergraphs, the hypergraphs with finite character. Finally, we present two examples showing that neither hypertree-width of hypergraph pairs nor hypertree-width of hypergraphs has the compactness property.

Minimal-change integrity maintenance using tuple deletions

We address the problem of minimal-change integrity maintenance in the context of integrity constraints in relational databases. We assume that integrity-restoration actions are limited to tuple deletions. We focus on two basic computational issues: repair checking (is a database instance a repair of a given database?) and consistent query answers [in: ACM Symposium on Principles of Database Systems (PODS), 1999, 68] (is a tuple an answer to a given query in every repair of a given database?). We study the computational complexity of both problems, delineating the boundary between the tractable and the intractable cases. We consider denial constraints, general functional and inclusion dependencies, as well as key and foreign key constraints. Our results shed light on the computational feasibility of minimal-change integrity maintenance. The tractable cases should lead to practical implementations. The intractability results highlight the inherent limitations of any integrity enforcement mechanism, e.g., triggers or referential constraint actions, as a way of performing minimal-change integrity maintenance.