Data management

About: Data management is a research topic. Over the lifetime, 31574 publications have been published within this topic receiving 424326 citations.

Data Intensive Distributed Computing: Challenges and Solutions for Large-scale Information Management

Abstract: The trend in scientific, as well as commercial, applications from a diverse range of fields has been towards being more and more data-intensive over time. Data Intensive Distributed Computing: Challenges and Solutions for Large-scale Information Management focuses on the challenges of distributed systems imposed by data intensive applications and on the different state-of-the-art solutions proposed to overcome such challenges. Providing hints on how to manage low-level data handling issues when performing data intensive distributed computing, this publication is ideal for scientists, researchers, engineers, and application developers, alike. With the knowledge of the correct data management techniques for their applications, readers will be able to focus on their primary goal, assured that their data management needs are handled reliably and efficiently.

Packaged warehouse solution system

Abstract: A data warehouse solution system comprises a metadata model, a user interface and an engine. The metadata model has an information needs model including metadata regarding information needs for building reports by users, and a data information model including metadata describing data that is available for building reports. The user interface has a customer user interface for presenting the information needs model to the users for report generation, and a modeling user interface for presenting the data information model to the users for manipulating data warehouse objects. The engine has a report management service unit for providing report management service using the information needs model, and a data management service unit for providing data management service including generation of a data warehouse using the data information model.

AIDE: An Active Learning-Based Approach for Interactive Data Exploration

Abstract: In this paper, we argue that database systems be augmented with an automated data exploration service that methodically steers users through the data in a meaningful way. Such an automated system is crucial for deriving insights from complex datasets found in many big data applications such as scientific and healthcare applications as well as for reducing the human effort of data exploration. Towards this end, we present AIDE, an Automatic Interactive Data Exploration framework that assists users in discovering new interesting data patterns and eliminate expensive ad-hoc exploratory queries. AIDE relies on a seamless integration of classification algorithms and data management optimization techniques that collectively strive to accurately learn the user interests based on his relevance feedback on strategically collected samples. We present a number of exploration techniques as well as optimizations that minimize the number of samples presented to the user while offering interactive performance. AIDE can deliver highly accurate query predictions for very common conjunctive queries with small user effort while, given a reasonable number of samples, it can predict with high accuracy complex disjunctive queries. It provides interactive performance as it limits the user wait time per iteration of exploration to less than a few seconds.