بایگانی برچسب برای: Data Models

Semantic Management of Streaming Data[taliem.ir]

Semantic Management of Streaming Data

One of the fundamental challenges facing the unprecedented data deluge produced by the sensor networks is how to manage time-series streaming data so that they can be reasoning-ready and provenance-aware. Semantic web technology shows great promise but lacks adequate support for the notion of time. We present a system for the representation, indexing and querying of time-series data, especially streaming data, using the semantic web approach. This system incorporates a special RDF vocabulary and a semantic interpretation for time relationships. The resulting framework, which we refer to as Time-Annotated RDF, provides basic functionality for the representation and querying of time-related data. The capabilities of Time-Annotated RDF were implemented as a suite of Java APIs on top of Tupelo, a semantic content management middleware, to provide transparent integration among heterogeneous data, as present in streams and other data sources, and their metadata. We show how this system supports commonly used time-related queries using TimeAnnotated SPARQL introduced in this paper as well as an analysis of the TA-RDF data model. Such prototype system has already seen successful usage in a virtual sensor project where near-real-time radar data streams need to be fetched, indexed, processed and re-published as new virtual sensor streams.
Reactive Power Generation Management for the[taliem.ir]

Reactive Power Generation Management for the Improvement of Power System Voltage Stability Margin

Voltage stability margin (VSM) of the power system relates to the reactive power reserves in the network. This paper presents a method to improve the VSM by generatorreactive power generation rescheduling. The management of the var generation formulated as an optimization problem and pseudo-gradient evolutionary programming (PGEP) was used to obtain the optimal solution. Modal analysis technique was used to guide the searching direction. Simulation results on the New England 39-bus system demonstrate that the proposed method is effective. Compared with the standard evolutionary programming (SEP), better solution can be obtained, and the convergence speed of the algorithm is improved also. The simulation results show that after the optimal reactive power rescheduling the reactive power reserves of the system is increased and the active/reactive power losses are decreased. The most important advantage is that, the voltage stability margin of power system can be improved without adding new var compensation equipment and changing the active power distribution.
Semantic Management of Streaming Data[taliem.ir]

Semantic Management of Streaming Data

One of the fundamental challenges facing the unprecedented data deluge produced by the sensor networks is how to manage time-series streaming data so that they can be reasoning-ready and provenance-aware. Semantic web technology shows great promise but lacks adequate support for the notion of time. We present a system for the representation, indexing and querying of time-series data, especially streaming data, using the semantic web approach. This system incorporates a special RDF vocabulary and a semantic interpretation for time relationships. The resulting framework, which we refer to as Time-Annotated RDF, provides basic functionality for the representation and querying of time-related data. The capabilities of Time-Annotated RDF were implemented as a suite of Java APIs on top of Tupelo, a semantic content management middleware, to provide transparent integration among heterogeneous data, as present in streams and other data sources, and their metadata. We show how this system supports commonly used time-related queries using Time Annotated SPARQL introduced in this paper as well as an analysis of the TA-RDF data model. Such prototype system has already seen successful usage in a virtual sensor project where near-real-time radar data streams need to be fetched, indexed, processed and re-published as new virtual sensor streams.
NoSQL Systems for Big Data Management[taliem.ir]

NoSQL Systems for Big Data Management

The advent of Big Data created a need for out-of-the-box horizontal scalability for data management systems. This ushered in an array of choices for Big Data management under the umbrella term NoSQL. In this paper, we provide a taxonomy and unified perspective on NoSQL systems. Using this perspective, we compare and contrast various NoSQL systems using multiple facets including system architecture, data model, query language, client API, scalability, and availability. We group current NoSQL systems into seven broad categories: Key-Value,Table-type/Column, Document, Graph, Native XML, Native Object, and Hybrid databases. We also describe application scenarios for each category to help the reader in choosing an appropriate NoSQL system for a given application. We conclude the paper by indicating future research directions.