FP6Индивидуална стипендия2007–2010

EXQUALIBUR · Quality-introspective data management system

6РП — Действия „Мария Кюри“

Период: 2007-09-01 → 2010-08-31
Финансиране от ЕС: 156 708 €
Участници: 2
Схема: OIF

Линиите свързват координатора с партньорите. За проекти отпреди 2014 г. CORDIS не винаги дава точни координати. Тези точки са на ниво град или държава.

Накратко на български

Системите за управление на данни се изследват чрез методи за откриване на грешки, като например дублирани записи или правописни сгрешки в големи масиви. Това помага за автоматичното подобряване на качеството на информацията и проследяването на грешките между различните източници.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Final Activity and Management Report Summary - EXQUALIBUR (Quality-Introspective Data Management System)

Data quality management has become one of the "hot topics" of emerging interest in various international academic and industrial communities involved in the management of data (Databases, Statistics, Workflow Management, Knowledge Engineering and Discovery from Databases). Multidisciplinary approaches are necessary to explore massive data sets, efficiently detect data quality problems (such as duplicates, errors, outliers, disguised missing values, misspellings, contradictions, inconsistencies, stale or incomplete data), correct errors, improve and ensure the quality of data in the databases. The overall objective of EXQUALIBUR project (MOIF-CT-2006-041000) was to propose theoretically founded solutions for controlling the quality of data with methods combining statistics, data mining and database engineering. The most important achievements made by EXQUALIBUR are at the frontier between Database System Engineering and Statistics for 1) detecting data anomalies and anomaly patterns in massive datasets and 2) tracing the propagation of errors from one data source to another and discover data source dependence and relationships. The methods proposed by EXQUALIBUR made an important step towards a new generation of quality-introspective data management systems, i.e., systems that are able to evaluate, control and improve automatically the quality of the data they store.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Real world data is never as perfect as we would like it to be and can often suffer from corruptions that may impact interpretations of the data, models created from data, and decisions made based on data. The problem of poor data quality stored in database -backed information systems is largely widespread in the governmental, commercial and industrial environments. It must be addressed urgently by theoretical and pragmatic approaches. These combined approaches should explore data, detect data quality problems (duplicates, errors, outliers, etc.), correct and ensure information quality in data management systems. Information quality is now one of the hot topics" in the international academic and industrial communities involved in the management of data. Useful and efficient methods and tools that continuously inspect and monitor data quality are necessary. Very little research has been aimed at methods to support such systems.The objectives of the proposed project are to propose theoretically founded solution s for controlling in an introspective way data quality in database systems using the reflection paradigm. Abstractly, reflection refers to the capability of a system to reason about and act upon itself. EXQUALIBUR is a multi-disciplinary project, at the frontier between Database Design, Knowledge Discovery and Statistics. Its outcome is a new generation of quality-introspective data management systems. The applicant is an experienced researcher; her contributions with quality-extended query processing and quality-aware data mining are the stepping-stones of the proposed project.The host institution is AT&T Labs, which provides an excellent environment for research. Her integration into AT&T Labs will enhance the applicant's expertise in integrated management and analysis of large data volumes. Concerning the return phase, the applicant plans to apply for professorship in Europe, and to manage an independent research group on quality-aware data management."

Оригинален текст от CORDIS (на английски).

Участници

AT&T ENTERPRISE SERVICES INC. (AT&T ESI) · FLORHAM PARK, NEW JERSEYНиво държаваСъединени щати