Data access

About: Data access is a research topic. Over the lifetime, 13141 publications have been published within this topic receiving 172859 citations. The topic is also known as: Data access.

Apparatus and method for providing private and shared access to host address and data spaces by guest programs in a virtual machine computer system

Abstract: A method and apparatus for providing common access to data spaces by a plurality of virtual machine guests emulated on a host computer system. A token received from a given one of the virtual machine guests is used to identify a particular host data space. The guest also supplies an offset. The offset and the identified host data space are then used to derive a host absolute address representative of a data location in the host data space.

Smart Waste Collection System Based on Location Intelligence

Abstract: Cities around the world are on the run to become smarter. Some of these have seen an opportunity on deploying dedicated municipal access networks to support all types of city management and maintenance services requiring a data connection. This paper practically demonstrates how Internet of Things (IoT) integration with data access networks, Geographic Information Systems (GIS), combinatorial optimization, and electronic engineering can contribute to improve cities’ management systems. We present a waste collection solution based on providing intelligence to trashcans, by using an IoT prototype embedded with sensors, which can read, collect, and transmit trash volume data over the Internet. This data put into a spatio-temporal context and processed by graph theory optimization algorithms can be used to dynamically and efficiently manage waste collection strategies. Experiments are carried out to investigate the benefits of such a system, in comparison to a traditional sectorial waste collection approaches, also including economic factors. A realistic scenario is set up by using Open Data from the city of Copenhagen, highlighting the opportunities created by this type of initiatives for third parties to contribute and develop Smart city solutions.

Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends.

Abstract: The emergence of massive datasets in a clinical setting presents both challenges and opportunities in data storage and analysis. This so called “big data” challenges traditional analytic tools and will increasingly require novel solutions adapted from other fields. Advances in information and communication technology present the most viable solutions to big data analysis in terms of efficiency and scalability. It is vital those big data solutions are multithreaded and that data access approaches be precisely tailored to large volumes of semi-structured/unstructured data. The MapReduce programming framework uses two tasks common in functional programming: Map and Reduce. MapReduce is a new parallel processing framework and Hadoop is its open-source implementation on a single computing node or on clusters. Compared with existing parallel processing paradigms (e.g. grid computing and graphical processing unit (GPU)), MapReduce and Hadoop have two advantages: 1) fault-tolerant storage resulting in reliable data processing by replicating the computing tasks, and cloning the data chunks on different computing nodes across the computing cluster; 2) high-throughput data processing via a batch processing framework and the Hadoop distributed file system (HDFS). Data are stored in the HDFS and made available to the slave nodes for computation. In this paper, we review the existing applications of the MapReduce programming framework and its implementation platform Hadoop in clinical big data and related medical health informatics fields. The usage of MapReduce and Hadoop on a distributed system represents a significant advance in clinical big data processing and utilization, and opens up new opportunities in the emerging era of big data analytics. The objective of this paper is to summarize the state-of-the-art efforts in clinical big data analytics and highlight what might be needed to enhance the outcomes of clinical big data analytics tools. This paper is concluded by summarizing the potential usage of the MapReduce programming framework and Hadoop platform to process huge volumes of clinical data in medical health informatics related fields.

Control of database access using security/user tag correspondence table

Abstract: A method and apparatus controls access to data in a database by configuring at least part of the database such that at least some of the data of the configured database is associated with a security tag, configuring a storage structure of user identifiers and associated user tags configuring a storage structure of user tags and associated security tags and mapping a user identifier to at least a sub-set of the data by determining from the storage structure of user tag and associated security tags a security tag or tags appropriate for the user tag of the user identifier and allowing access to the data from the configured database associated with the security tag or tags. By providing a storage structure of user tags and associated security tags it is possible to change the security policy by modification of the data in the storage structure alone without any need to modify the data in the configured database.

System and method for data rights management

Abstract: A system and method for data rights management across multiple data rights management architectures is disclosed. The system and method solves the problems posed by multiple incompatible data rights management architectures. In particular, a data rights management clearing house is provided that generates permits, permit classes, and enables content packaging across multiple data rights management architectures. Consumers may acquire rights to content packaged with different data rights management architecture from the single data rights management clearing house. Additionally, the system and method enables content packagers to package content with multiple data rights management architectures. Finally, the data rights management clearing house provides consumers with a single location from which to manage data access rights and restore data access rights that have been lost.