Data management

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

Here are my Data Files. Here are my Queries. Where are my Results

Abstract: Database management systems (DBMS) provide incredible exibility and performance when it comes to query processing, scalability and accuracy. To fully exploit DBMS features, however, the user must dene a schema, load the data, tune the system for the expected workload, and answer several questions. Should the database use a column-store, a row-store or some hybrid format? What indices should be created? All these questions make for a formidable and time-consuming hurdle, often deterring new applications or imposing high cost to existing ones. A characteristic example is that of scientic databases with huge data sets. The prohibitive initialization cost and complexity still forces scientists to rely on \ancient" tools for their data management tasks, delaying scientic understanding and progress.

Data literacy for researchers and data librarians

Abstract: This paper describes data literacy and emphasizes its importance. Data literacy is vital for researchers who need to become data literate science workers and also for (potential) data management pr...

SMARTDIAB: A Communication and Information Technology Approach for the Intelligent Monitoring, Management and Follow-up of Type 1 Diabetes Patients

Abstract: SMARTDIAB is a platform designed to support the monitoring, management, and treatment of patients with type 1 diabetes mellitus (T1DM), by combining state-of-the-art approaches in the fields of database (DB) technologies, communications, simulation algorithms, and data mining. SMARTDIAB consists mainly of two units: 1) the patient unit (PU); and 2) the patient management unit (PMU), which communicate with each other for data exchange. The PMU can be accessed by the PU through the internet using devices, such as PCs/laptops with direct internet access or mobile phones via a Wi-Fi/General Packet Radio Service access network. The PU consists of an insulin pump for subcutaneous insulin infusion to the patient and a continuous glucose measurement system. The aforementioned devices running a user-friendly application gather patient's related information and transmit it to the PMU. The PMU consists of a diabetes data management system (DDMS), a decision support system (DSS) that provides risk assessment for long-term diabetes complications, and an insulin infusion advisory system (IIAS), which reside on a Web server. The DDMS can be accessed from both medical personnel and patients, with appropriate security access rights and front-end interfaces. The DDMS, apart from being used for data storage/retrieval, provides also advanced tools for the intelligent processing of the patient's data, supporting the physician in decision making, regarding the patient's treatment. The IIAS is used to close the loop between the insulin pump and the continuous glucose monitoring system, by providing the pump with the appropriate insulin infusion rate in order to keep the patient's glucose levels within predefined limits. The pilot version of the SMARTDIAB has already been implemented, while the platform's evaluation in clinical environment is being in progress.

Self-Adaptive Data Collection and Fusion for Health Monitoring Based on Body Sensor Networks

Abstract: In the past few years, wireless body sensor networks (WBSNs) emerged as a low-cost solution for healthcare applications. In WBSNs, biosensors collect periodically physiological measurement and send them to the coordinator where the data fusion process takes place. However, processing the huge amount of data captured by the limited lifetime biosensors and taking the right decisions when there is an emergency are major challenges in WBSNs. In this paper, we introduce a biosensor data management framework, starting from data collection to decision making. First, we propose an adaptive data collection approach on the biosensor node level. This approach uses an early warning score system to optimize data transmission and estimates in real time the sensing frequency. Second, we present a data fusion model on the coordinator level using a decision matrix and fuzzy set theory. To evaluate our approach, we conducted multiple series of simulations on real sensor data. The results show that our approach reduces the amount of collected data, while maintaining data integrity. In addition, we show the impact of sampling and filtering data on the accuracy of the taken decisions and compare our data fusion approach with a basic decision tree algorithm.

Demonstration of the Myria big data management service

Abstract: In this demonstration, we will showcase Myria, our novel cloud service for big data management and analytics designed to improve productivity. Myria's goal is for users to simply upload their data and for the system to help them be self-sufficient data science experts on their data -- self-serve analytics. Using a web browser, Myria users can upload data, author efficient queries to process and explore the data, and debug correctness and performance issues. Myria queries are executed on a scalable, parallel cluster that uses both state-of-the-art and novel methods for distributed query processing. Our interactive demonstration will guide visitors through an exploration of several key Myria features by interfacing with the live system to analyze big datasets over the web.