An Extensible Framework for Data Cleaning

Projet CARAVELData integration solutions dealing with large amounts of data have been strongly required in the last few years. Besides the traditional data integration problems (e.g. schema integration, local to global schema mappings), three additional data problems have to be dealt with: (1) the absence of universal keys across different databases that is known as the object identity problem, (2) the existence of keyborad errors in the data, and (3) the presence of inconsistencies in data coming from multiple sources. Dealing with these problems is globally called the data cleaning process. In this work, we propose a framework which offers the fundamental services required by this process: data transformation, duplicate elimination and multi-table matching. These services are implemented using a set of purposely designed macro-operators. Moreover, we propose an SQL extension for specifying each of the macro-operators. One important feature of the framework is the ability of explicitly including the human interaction in the process. The main novelty of the work is that the framework permits the following performance optimizations which are tailored for data cleaning applications: mixed evaluation, neighborhood hash join, decision push-down and short-circuited computation. We measure the benefits of each

spChains: A Declarative Framework for Data Stream Processing in Pervasive Applications

Pervasive applications rely on increasingly complex streams of sensor data continuously captured from the physical world. Such data is crucial to enable applications to ``understand'' the current context and to infer the right actions to perform, be they fully automatic or involving some user decisions. However, the continuous nature of such streams, the relatively high throughput at which data is generated and the number of sensors usually deployed in the environment, make direct data handling practically unfeasible. Data not only needs to be cleaned, but it must also be filtered and aggregated to relieve higher level algorithms from near real-time handling of such massive data flows. We propose here a stream-processing framework (spChains), based upon state-of-the-art stream processing engines, which enables declarative and modular composition of stream processing chains built atop of a set of extensible stream processing blocks. While stream processing blocks are delivered as a standard, yet extensible, library of application-independent processing elements, chains can be defined by the pervasive application engineering team. We demonstrate the flexibility and effectiveness of the spChains framework on two real-world applications in the energy management and in the industrial plant management domains, by evaluating them on a prototype implementation based on the Esper stream processo

Extracting, Transforming and Archiving Scientific Data

It is becoming common to archive research datasets that are not only large but also numerous. In addition, their corresponding metadata and the software required to analyse or display them need to be archived. Yet the manual curation of research data can be difficult and expensive, particularly in very large digital repositories, hence the importance of models and tools for automating digital curation tasks. The automation of these tasks faces three major challenges: (1) research data and data sources are highly heterogeneous, (2) future research needs are difficult to anticipate, (3) data is hard to index. To address these problems, we propose the Extract, Transform and Archive (ETA) model for managing and mechanizing the curation of research data. Specifically, we propose a scalable strategy for addressing the research-data problem, ranging from the extraction of legacy data to its long-term storage. We review some existing solutions and propose novel avenues of research.Comment: 8 pages, Fourth Workshop on Very Large Digital Libraries, 201