We investigate the problem of building least squares regression models over training datasets defined by arbitrary join queries on database tables. Our key observation is that joins entail a high degree of redundancy in both computation and data representation, which is not required for the end-to-end solution to learning over joins.We propose a new paradigm for computing batch gradient descent that exploits the factorized computation and representation of the training datasets, a rewriting of the regression objective function that decouples the computation of cofactors of model parameters from their convergence, and the commutativity of cofactor computation with relational union and projection. We introduce three flavors of this approach: F/FDB computes the cofactors in one pass over the materialized factorized join; F avoids this materialization and intermixes cofactor and join computation; F/SQL expresses this mixture as one SQL query.Our approach has the complexity of join factorization, which can be exponentially lower than of standard joins. Experiments with commercial, public, and synthetic datasets show that it outperforms MADlib, Python StatsModels, and R, by up to three orders of magnitude.

Massive graph data sets are pervasive in contemporary application domains. Hence, graph database systems are becoming increasingly important. In the experimental study of these systems, it is vital that the research community has shared solutions for the generation of database instances and query workloads having predictable and controllable properties. In this paper, we present the design and engineering principles of gMark, a domain- and query language-independent graph instance and query workload generator. A core contribution of gMark is its ability to target and control the diversity of properties of both the generated instances and the generated workloads coupled to these instances. Further novelties include support for regular path queries, a fundamental graph query paradigm, and schema-driven selectivity estimation of queries, a key feature in controlling workload chokepoints. We illustrate the flexibility and practical usability of gMark by showcasing the framework's capabilities in generating high quality graphs and workloads, and its ability to encode user-defined schemas across a variety of application domains.Comment: Accepted in November 2016. URL: http://ieeexplore.ieee.org/document/7762945/. in IEEE Transactions on Knowledge and Data Engineering 201

Abstract-Massive graph data sets are pervasive in contemporary application domains. Hence, graph database systems are becoming increasingly important. In the experimental study of these systems, it is vital that the research community has shared solutions for the generation of database instances and query workloads having predictable and controllable properties. In this paper, we present the design and engineering principles of gMark, a domain-and query language-independent graph instance and query workload generator. A core contribution of gMark is its ability to target and control the diversity of properties of both the generated instances and the generated workloads coupled to these instances. Further novelties include support for regular path queries, a fundamental graph query paradigm, and schema-driven selectivity estimation of queries, a key feature in controlling workload chokepoints. We illustrate the flexibility and practical usability of gMark by showcasing the framework's capabilities in generating high quality graphs and workloads, and its ability to encode user-defined schemas across a variety of application domains.

Given the maturity of the data integration field it is surprising that rigorous empirical evaluations of research ideas are so scarce. We identify one major roadblock for empirical work-the lack of comprehensive metadata generators that can be used to create benchmarks for different integration tasks. This makes it difficult to compare integration solutions, understand their generality, and understand their performance. We present iBench, the first metadata generator that can be used to evaluate a wide-range of integration tasks (data exchange, mapping creation, mapping composition, schema evolution, among many others). iBench permits control over the size and characteristics of the metadata it generates (schemas, constraints, and mappings). We show that iBench can be used to create very large, complex, yet realistic scenarios. Our evaluation of iBench demonstrates that it can efficiently generate large scenarios with different characteristics. We also present an evaluation of two mapping creation systems using iBench and show that the intricate control that iBench provides over metadata scenarios can reveal new and important empirical insights into integration solutions. iBench is an open-source, extensible tool that we are providing to the community. We believe it will raise the bar for empirical evaluation and comparison of data integration systems.