Empowering In-Memory Relational Database Engines with Native Graph Processing

The plethora of graphs and relational data give rise to many interesting graph-relational queries in various domains, e.g., finding related proteins satisfying relational predicates in a biological network. The maturity of RDBMSs motivated academia and industry to invest efforts in leveraging RDBMSs for graph processing, where efficiency is proven for vital graph queries. However, none of these efforts process graphs natively inside the RDBMS, which is particularly challenging due to the impedance mismatch between the relational and the graph models. In this paper, we propose to treat graphs as first-class citizens inside the relational engine so that operations on graphs are executed natively inside the RDBMS. We realize our approach inside VoltDB, an open-source in-memory relational database, and name this realization GRFusion. The SQL and the query engine of GRFusion are empowered to declaratively define graphs and execute cross-data-model query plans formed by graph and relational operators, resulting in up to four orders-of-magnitude in query-time speedup w.r.t. state-of-the-art approaches

Declarative Data Analytics: a Survey

The area of declarative data analytics explores the application of the declarative paradigm on data science and machine learning. It proposes declarative languages for expressing data analysis tasks and develops systems which optimize programs written in those languages. The execution engine can be either centralized or distributed, as the declarative paradigm advocates independence from particular physical implementations. The survey explores a wide range of declarative data analysis frameworks by examining both the programming model and the optimization techniques used, in order to provide conclusions on the current state of the art in the area and identify open challenges.Comment: 36 pages, 2 figure

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

Scaling Datalog for Machine Learning on Big Data

In this paper, we present the case for a declarative foundation for data-intensive machine learning systems. Instead of creating a new system for each specific flavor of machine learning task, or hardcoding new optimizations, we argue for the use of recursive queries to program a variety of machine learning systems. By taking this approach, database query optimization techniques can be utilized to identify effective execution plans, and the resulting runtime plans can be executed on a single unified data-parallel query processing engine. As a proof of concept, we consider two programming models--Pregel and Iterative Map-Reduce-Update---from the machine learning domain, and show how they can be captured in Datalog, tuned for a specific task, and then compiled into an optimized physical plan. Experiments performed on a large computing cluster with real data demonstrate that this declarative approach can provide very good performance while offering both increased generality and programming ease

Scientific Workflows and Provenance: Introduction and Research Opportunities

Scientific workflows are becoming increasingly popular for compute-intensive and data-intensive scientific applications. The vision and promise of scientific workflows includes rapid, easy workflow design, reuse, scalable execution, and other advantages, e.g., to facilitate "reproducible science" through provenance (e.g., data lineage) support. However, as described in the paper, important research challenges remain. While the database community has studied (business) workflow technologies extensively in the past, most current work in scientific workflows seems to be done outside of the database community, e.g., by practitioners and researchers in the computational sciences and eScience. We provide a brief introduction to scientific workflows and provenance, and identify areas and problems that suggest new opportunities for database research.Comment: 12 pages, 2 figure

SOFA: An Extensible Logical Optimizer for UDF-heavy Dataflows

Recent years have seen an increased interest in large-scale analytical dataflows on non-relational data. These dataflows are compiled into execution graphs scheduled on large compute clusters. In many novel application areas the predominant building blocks of such dataflows are user-defined predicates or functions (UDFs). However, the heavy use of UDFs is not well taken into account for dataflow optimization in current systems. SOFA is a novel and extensible optimizer for UDF-heavy dataflows. It builds on a concise set of properties for describing the semantics of Map/Reduce-style UDFs and a small set of rewrite rules, which use these properties to find a much larger number of semantically equivalent plan rewrites than possible with traditional techniques. A salient feature of our approach is extensibility: We arrange user-defined operators and their properties into a subsumption hierarchy, which considerably eases integration and optimization of new operators. We evaluate SOFA on a selection of UDF-heavy dataflows from different domains and compare its performance to three other algorithms for dataflow optimization. Our experiments reveal that SOFA finds efficient plans, outperforming the best plans found by its competitors by a factor of up to 6

Information Integration and Computational Logic

Information Integration is a young and exciting field with enormous research and commercial significance in the new world of the Information Society. It stands at the crossroad of Databases and Artificial Intelligence requiring novel techniques that bring together different methods from these fields. Information from disparate heterogeneous sources often with no a-priori common schema needs to be synthesized in a flexible, transparent and intelligent way in order to respond to the demands of a query thus enabling a more informed decision by the user or application program. The field although relatively young has already found many practical applications particularly for integrating information over the World Wide Web. This paper gives a brief introduction of the field highlighting some of the main current and future research issues and application areas. It attempts to evaluate the current and potential role of Computational Logic in this and suggests some of the problems where logic-based techniques could be used.Comment: 53 Page

Big Data Systems Meet Machine Learning Challenges: Towards Big Data Science as a Service

Recently, we have been witnessing huge advancements in the scale of data we routinely generate and collect in pretty much everything we do, as well as our ability to exploit modern technologies to process, analyze and understand this data. The intersection of these trends is what is called, nowadays, as Big Data Science. Cloud computing represents a practical and cost-effective solution for supporting Big Data storage, processing and for sophisticated analytics applications. We analyze in details the building blocks of the software stack for supporting big data science as a commodity service for data scientists. We provide various insights about the latest ongoing developments and open challenges in this domain

Unicast and Multicast Qos Routing with Soft Constraint Logic Programming

We present a formal model to represent and solve the unicast/multicast routing problem in networks with Quality of Service (QoS) requirements. To attain this, first we translate the network adapting it to a weighted graph (unicast) or and-or graph (multicast), where the weight on a connector corresponds to the multidimensional cost of sending a packet on the related network link: each component of the weights vector represents a different QoS metric value (e.g. bandwidth, cost, delay, packet loss). The second step consists in writing this graph as a program in Soft Constraint Logic Programming (SCLP): the engine of this framework is then able to find the best paths/trees by optimizing their costs and solving the constraints imposed on them (e.g. delay < 40msec), thus finding a solution to QoS routing problems. Moreover, c-semiring structures are a convenient tool to model QoS metrics. At last, we provide an implementation of the framework over scale-free networks and we suggest how the performance can be improved.Comment: 45 page

AppLP: A Dialogue on Applications of Logic Programming

This document describes the contributions of the 2016 Applications of Logic Programming Workshop (AppLP), which was held on October 17 and associated with the International Conference on Logic Programming (ICLP) in Flushing, New York City.Comment: David S. Warren and Yanhong A. Liu (Editors). 33 pages. Including summaries by Christopher Kane and abstracts or position papers by M. Aref, J. Rosenwald, I. Cervesato, E.S.L. Lam, M. Balduccini, J. Lobo, A. Russo, E. Lupu, N. Leone, F. Ricca, G. Gupta, K. Marple, E. Salazar, Z. Chen, A. Sobhi, S. Srirangapalli, C.R. Ramakrishnan, N. Bj{\o}rner, N.P. Lopes, A. Rybalchenko, and P. Tara