Schema Vacuuming in Temporal Databases

Temporal databases facilitate the support of historical information by providing functions for indicating the intervals during which a tuple was applicable (along one or more temporal dimensions). Because data are never deleted, only superceded, temporal databases are inherently append-only resulting, over time, in a large historical sequence of database states. Data vacuuming in temporal databases allows for this sequence to be shortened by strategically, and irrevocably, deleting obsolete data. Schema versioning allows users to maintain a history of database schemata without compromising the semantics of the data or the ability to view data through historical schemata. While the techniques required for data vacuuming in temporal databases have been relatively well covered, the associated area of vacuuming schemata has received less attention. This paper discusses this issue and proposes a mechanism that fits well with existing methods for data vacuuming and schema versioning

Achieving a Sequenced, Relational Query Language with Log-Segmented Timestamps

In a relational temporal database, typically each row of each table has a period timestamp to indicate the lifetime of that row. In order to evaluate a query in a temporal database, sequenced semantics comes into play. The semantics stipulates that the query must be evaluated simultaneously in each time instant using the data rows available at that point of time. Existing researches have proposed changes in the query evaluation engine to achieve sequenced semantics. In this paper we show a way to support sequenced semantics without modifying the query engine. We propose a noble construction log-segmented label to represent the lifetime and replace the period timestamp from each row with a log-segmented label that signifies when the tuple is alive. Then we translate a sequenced query to a non-temporal query by utilizing the properties of log-segmented label. The translated query has only operations already available in a typical relational database making the query readily executable in an unaltered installation of the database. Thus the sequenced query inevitably runs and retrieve data without changing query evaluation engine. Finally our implementation using Java language, ANTLR parser generator and PostgreSQL database demonstrates the feasibility of the proposed mechanism, which, to the best of our knowledge, has not been previously shown

DataHub: Collaborative Data Science & Dataset Version Management at Scale

Relational databases have limited support for data collaboration, where teams collaboratively curate and analyze large datasets. Inspired by software version control systems like git, we propose (a) a dataset version control system, giving users the ability to create, branch, merge, difference and search large, divergent collections of datasets, and (b) a platform, DataHub, that gives users the ability to perform collaborative data analysis building on this version control system. We outline the challenges in providing dataset version control at scale.Comment: 7 page

Supporting service discovery, querying and interaction in ubiquitous computing environments.

In this paper, we contend that ubiquitous computing environments will be highly heterogeneous, service rich domains. Moreover, future applications will consequently be required to interact with multiple, specialised service location and interaction protocols simultaneously. We argue that existing service discovery techniques do not provide sufficient support to address the challenges of building applications targeted to these emerging environments. This paper makes a number of contributions. Firstly, using a set of short ubiquitous computing scenarios we identify several key limitations of existing service discovery approaches that reduce their ability to support ubiquitous computing applications. Secondly, we present a detailed analysis of requirements for providing effective support in this domain. Thirdly, we provide the design of a simple extensible meta-service discovery architecture that uses database techniques to unify service discovery protocols and addresses several of our key requirements. Lastly, we examine the lessons learnt through the development of a prototype implementation of our architecture