Curated databases in bioinformatics and other disciplines are the result of a great deal of manual annotation, correction and transfer of data from other sources. Provenance information concerning the creation, attribution, or version history of such data is crucial for assessing its integrity and scientific value. General purpose database systems provide little support for tracking provenance, especially when data moves among databases. This paper investigates general-purpose techniques for recording provenance for data that is copied among databases. We describe an approach in which we track the user's actions while browsing source databases and copying data into a curated database, in order to record the user's actions in a convenient, queryable form. We present an implementation of this technique and use it to evaluate the feasibility of database support for provenance management. Our experiments show that although the overhead of a naïve approach is fairly high, it can be decreased to an acceptable level using simple optimizations.

Abstract. This paper describes the overall design and architecture of the Timber XML database system currently being implemented at the University of Michigan. The system is based upon a bulk algebra for manipulating trees, and natively stores XML. New access methods have been developed to evaluate queries in the XML context, and new cost estimation and query optimization techniques have also been developed. We present performance numbers to support some of our design decisions. We believe that the key intellectual contribution of this system is a comprehensive set-at-a-time query processing ability in a native XML store, with all the standard components of relational query processing, including algebraic rewriting and a cost-based optimizer.

Database researchers have striven to improve the capability of a database in terms of both performance and functionality. We assert that the usability of a database is as important as its capability. In this paper, we study why database systems today are so difficult to use. We identify a set of five pain points and propose a research agenda to address these. In particular, we introduce a presentation data model and recommend direct data manipulation with a schema later approach. We also stress the importance of provenance and of consistency across presentation models.

Generating value from data requires the ability to find, access and make sense of datasets. There are many efforts underway to encourage data sharing and reuse, from scientific publishers asking authors to submit data alongside manuscripts to data marketplaces, open data portals and data communities. Google recently beta released a search service for datasets, which allows users to discover data stored in various online repositories via keyword queries. These developments foreshadow an emerging research field around dataset search or retrieval that broadly encompasses frameworks, methods and tools that help match a user data need against a collection of datasets. Here, we survey the state of the art of research and commercial systems in dataset retrieval. We identify what makes dataset search a research field in its own right, with unique challenges and methods and highlight open problems. We look at approaches and implementations from related areas dataset search is drawing upon, including information retrieval, databases, entity-centric and tabular search in order to identify possible paths to resolve these open problems as well as immediate next steps that will take the field forward.

As the world is increasingly networked and digitized, the data we store has more and more frequently been chopped, baked, diced and stewed. In consequence, there is an increasing need to store and manage provenance for each data item stored in a database, describing exactly where it came from, and what manipulations have been applied to it. Storage of the complete provenance of each data item can become prohibitively expensive. In this paper, we identify important properties of provenance that can be used to considerably reduce the amount of storage required. We identify three different techniques: a family of factorization processes and two methods based on inheritance, to decrease the amount of storage required for provenance.We have used the techniques described in this work to significantly reduce the provenance storage costs associated with constructing MiMI [22], a warehouse of data regarding protein interactions, as well as two provenance stores, Karma [31] and PReServ [20], produced through workflow execution. In these real provenance sets, we were able to reduce the size of the provenance by up to a factor of 20. Additionally, we show that this reduced store can be queried efficiently and further that incremental changes can be made inexpensively.