Boris Glavic

Perm: Processing Provenance and Data on the Same Data Model through Query Rewriting

Data provenance is information that describes how a given data item was produced. The provenance includes source and intermediate data as well as the transformations involved in producing the concrete data item. In the context of a relational databases, the source and intermediate dataitems are relations, tuples and attribute values. The transformations are SQL queries and/or functions on the relational data items. Existing approaches capture provenance information by extending the underlying data model. This has the intrinsic disadvantage that the provenance must be stored and accessed using a different model than the actual data. In this paper, we present an alternative approach that uses query rewriting to annotate result tuples with provenance information. The rewritten query and its result use the same model and can, thus, be queried, stored and optimized using standard relational database techniques. In the paper we formalize the query rewriting procedures, prove their correctness, and evaluate a first implementation of the ideas using PostgreSQL. As the experiments indicate, our approach efficiently provides provenance information inducing only a small overhead on normal operations.

Big Data Provenance: Challenges and Implications for Benchmarking

Data Provenance is information about the origin and creation process of data. Such information is useful for debugging data and transformations, auditing, evaluating the quality of and trust in data, modelling authenticity, and implementing access control for derived data. Provenance has been studied by the database, workflow, and distributed systems communities, but provenance for Big Data - which we refer to as Big Provenance - is a largely unexplored field. This paper reviews existing approaches for large-scale distributed provenance and discusses potential challenges for Big Data benchmarks that aim to incorporate provenance data/management. Furthermore, we will examine how Big Data benchmarking could benefit from different types of provenance information. We argue that provenance can be used for identifying and analyzing performance bottlenecks, to compute performance metrics, and to test a systems ability to exploit commonalities in data and processing.

Using SQL for Efficient Generation and Querying of Provenance Information

In applications such as data warehousing or data exchange, the ability to efficiently generate and query provenance information is crucial to understand the origin of data. In this chapter, we review some of the main contributions of Perm, a DBMS that generates different types of provenance information for complex SQL queries (including nested and correlated subqueries and aggregation). The two key ideas behind Perm are representing data and its provenance together in a single relation and relying on query rewrites to generate this representation. Through this, Perm supports fully integrated, on-demand provenance generation and querying using SQL. Since Perm rewrites a query requesting provenance into a regular SQL query and generates easily optimizable SQL code, its performance greatly benefits from the query optimization techniques provided by the underlying DBMS.

TRAMP: understanding the behavior of schema mappings through provenance

Though partially automated, developing schema mappings remains a complex and potentially error-prone task. In this paper, we present TRAMP (TRAnsformation Mapping Provenance), an extensive suite of tools supporting the debugging and tracing of schema mappings and transformation queries. TRAMP combines and extends data provenance with two novel notions, transformation provenance and mapping provenance, to explain the relationship between transformed data and those transformations and mappings that produced that data. In addition we provide query support for transformations, data, and all forms of provenance. We formally define transformation and mapping provenance, present an efficient implementation of both forms of provenance, and evaluate the resulting system through extensive experiments.Though partially automated, developing schema mappings remains a complex and potentially error-prone task. In this paper, we present TRAMP (TRAnsformation Mapping Provenance), an extensive suite of tools supporting the debugging and tracing of schema mappings and transformation queries. TRAMP combines and extends data provenance with two novel notions, transformation provenance and mapping provenance, to explain the relationship between transformed data and those transformations and mappings that produced that data. In addition we provide query support for transformations, data, and all forms of provenance. We formally define transformation and mapping provenance, present an efficient implementation of both forms of provenance, and evaluate the resulting system through extensive experiments.

Provenance for nested subqueries

Data provenance is essential in applications such as scientific computing, curated databases, and data warehouses. Several systems have been developed that provide provenance functionality for the relational data model. These systems support only a subset of SQL, a severe limitation in practice since most of the application domains that benefit from provenance information use complex queries. Such queries typically involve nested subqueries, aggregation and/or user defined functions. Without support for these constructs, a provenance management system is of limited use. In this paper we address this limitation by exploring the problem of provenance derivation when complex queries are involved. More precisely, we demonstrate that the widely used definition of Why-provenance fails in the presence of nested subqueries, and show how the definition can be modified to produce meaningful results for nested subqueries. We further present query rewrite rules to transform an SQL query into a query propagating provenance. The solution introduced in this paper allows us to track provenance information for a far wider subset of SQL than any of the existing approaches. We have incorporated these ideas into the Perm provenance management system engine and used it to evaluate the feasibility and performance of our approach.

The perm provenance management system in action

In this demonstration we present the Perm provenance management system (PMS). Perm is capable of computing, storing and querying provenance information for the relational data model. Provenance is computed by using query rewriting techniques to annotate tuples with provenance information. Thus, provenance data and provenance computations are represented as relational data and queries and, hence, can be queried, stored and optimized using standard relational database techniques. This demo shows the complete Perm system and lets attendants examine in detail the process of query rewriting and provenance retrieval in Perm, the most complete data provenance system available today. For example, Perm supports lazy and eager provenance computation, external provenance and various contribution semantics.

The iBench integration metadata generator

Given the maturity of the data integration field it is surprising that rigorous empirical evaluations of research ideas are so scarce. We identify a 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). Our evaluation demonstrates that iBench can efficiently generate very large, complex, yet realistic scenarios with different characteristics. We also present an evaluation of three mapping creation systems using iBench and show that the intricate control that iBench provides over metadata scenarios can reveal new and important empirical insights. 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.

Clustering multidimensional sequences in spatial and temporal databases

Many environmental, scientific, technical or medical database applications require effective and efficient mining of time series, sequences or trajectories of measurements taken at different time points and positions forming large temporal or spatial databases. Particularly the analysis of concurrent and multidimensional sequences poses new challenges in finding clusters of arbitrary length and varying number of attributes. We present a novel algorithm capable of finding parallel clusters in different subspaces and demonstrate our results for temporal and spatial applications. Our analysis of structural quality parameters in rivers is successfully used by hydrologists to develop measures for river quality improvements.

Ariadne: managing fine-grained provenance on data streams

Managing fine-grained provenance is a critical requirement for data stream management systems (DSMS), not only to address complex applications that require diagnostic capabilities and assurance, but also for providing advanced functionality such as revision processing or query debugging. This paper introduces a novel approach that uses operator instrumentation, i.e., modifying the behavior of operators, to generate and propagate fine-grained provenance through several operators of a query network. In addition to applying this technique to compute provenance eagerly during query execution, we also study how to decouple provenance computation from query processing to reduce run-time overhead and avoid unnecessary provenance retrieval. This includes computing a concise superset of the provenance to allow lazily replaying a query network and reconstruct its provenance as well as lazy retrieval to avoid unnecessary reconstruction of provenance. We develop stream-specific compression methods to reduce the computational and storage overhead of provenance generation and retrieval. Ariadne, our provenance-aware extension of the Borealis DSMS implements these techniques. Our experiments confirm that Ariadne manages provenance with minor overhead and clearly outperforms query rewrite, the current state-of-the-art.

Messing up with BART: error generation for evaluating data-cleaning algorithms

We study the problem of introducing errors into clean databases for the purpose of benchmarking data-cleaning algorithms. Our goal is to provide users with the highest possible level of control over the error-generation process, and at the same time develop solutions that scale to large databases. We show in the paper that the error-generation problem is surprisingly challenging, and in fact, NP-complete. To provide a scalable solution, we develop a correct and efficient greedy algorithm that sacrifices completeness, but succeeds under very reasonable assumptions. To scale to millions of tuples, the algorithm relies on several non-trivial optimizations, including a new symmetry property of data quality constraints. The trade-off between control and scalability is the main technical contribution of the paper.