12195287
Towards service collaboration model in grid-based zero latency data stream warehouse (GZLDSWH)
A grid-based zero-latency data stream warehouse (GZLDSWH), built upon a set of OGSI-based grid services and GT3 toolkit, overcomes the resource limitation issue for data stream processing without using traditional approximate approaches. However, due to its "automated event-based reaction" characteristic, the GZLDSWH requires a mechanism which allows the grid services to be able to work together to fulfill the common tasks. This paper describes the collaboration model for the grid services which enables the automation of the GZLDSWH in capturing and storing continuous data streams, making analytical processing, and reacting autonomously in near real time with some kinds of events based on well-established knowledge base.
7825329
Zero-Latency Data Warehousing for Heterogeneous Data Sources and Continuous Data Streams
In this paper, a framework for building an overall Zero-Latency Data Warehouse system (ZLDWH) is provided. Such a ZLDWH requires tasks such as data changes detection, continuous loading and updating of new data, autonomous analysis and the execution of actions to be completed in the Data Warehouse (DWH). For this purpose, we combine (1) an existing solution for the continuous data integration and (2) the known approach of Active Data Warehousing (ADWH) by introducing protocols that enable the correct collaboration between the two. The continuous data integration is realized on the one hand by asynchronous communication between multiple information sources and the DWH through a message queuing system, and on the other hand by the continuous loading and updating. To obtain a low-latency time for this cycle, an “active rule evaluation” is provided between the data feeding stage and its loading into the DWH. This is realized by applying ECA-Techniques (EventCondition-Action) to obtain a more “active” characteristic of the DWH. A second goal of this paper is to take into consideration a special class of information source namely continuous data streams. Continuous data streams are information sources in which data arrives in high-volume, rapidly, bursty, and needs to be processed continuously. Therefore novel protocols and techniques are needed for capturing, storing and evaluate analysis decision based on new coming data from such continuous data stream sources.
6251517
A Front-End Approach for User Query Generation and Information Retrieval in the SemanticLIFE Framework
Formulating unambiguous queries in the Semantic Web applications is a challenging task for users. This paper presents a new approach in guiding users to generate clear requests based on their common nature of querying for information. The approach known as the "front-end approach" gives users an overview about the system data through a "virtual data component" which stores the extracted metadata of the data storage sources in the form of an ontology. This approach reduces the ambiguities in users' requests at very early stage; and allows the query refinement process to easily to fulfill users's demands. Furthermore, the approach provide a powerful query engine, called "context-based querying", that recommends the appropriate query patterns according to the user's querying context. These features help the user in generating clear query more easier.
1402747
'SemanticLIFE' - A Framework for Managing Information of A Human Lifetime
The SemanticLIFE' system is designed to store, manage and retrieve ones lifetime’s information entities. It enables the acquisition and storage of data while giving annotations to email messages, browsed web pages, phone calls, images, contacts, life events and other resources. 'SemanticLIFE' 
also provides intuitive and effective search mechanism based upon stored semantics. This paper presents the framework for the system to devise lifetime data store and search using recognized standards. We are aiming at a system which supports the long term memory by associating metadata with content and ontologies. The ultimate goal of our project is to build a Personal Information Management system over a Human Lifetime using ontologies as a basis for the representation of its content.
16854388
Benchmarking Global Optimization and Constraint Satisfaction Codes
A benchmarking suite describing over 1000 optimization problems and constraint satisfaction problems covering problems from different traditions is described, annotated with best known solutions, and accompanied by recommended benchmarking protocols for comparing test results.
534199
GRAVY: Towards Virtual File System for the Grid
Today large-scale applications require access to data stored in heterogeneous storage systems located at geographically distributed virtual organizations. In such applications, users are forced to deal with different administrative policies and structures at each site, and various data access mechanisms on each storage system. This implies a lot of human interventions in order to develop dedicated programs and scripts for data transfer between theses systems. This paper presents GRid-enAbled Virtual file sYstem (GRAVY) architecture which enables the automation of data transfers between distributed file systems irrespective of their heterogeneity. This feature enables high-level schedulers integrated with GRAVY to control data placements like computational jobs. GRAVY supports multiple data access protocols and provides an easy-to-use interface for novice Grid users.
6695051
Ontology Driven Concept Approximation
This paper investigates the concept approximation problem using ontology as an domain knowledge representation model and rough set theory. In [7] [8], we have presented a rough set based multi-layered learning framework for approximation of complex concepts assuming the existence of a simple concept hierarchy. The proposed methodology utilizes the ontology structure to learn compound concepts using the rough approximations of the primitive concepts as input attributes. In this paper we consider the extended model for knowledge representation where the concept hierarchies are embedded with additional knowledge in a form of relations or constrains among sub-concepts. We present an extended multi-layered learning scheme that can incorporate the additional knowledge and propose some classes of such relations that assure an improvement of the learning algorithm as well as a convenience of the knowledge modeling process. We illustrate the proposed method and present some results of experiment with data from sunspot recognition problem.
17197783
Multiple sender distributed video streaming
With the explosive growth of video applications over the Internet, many approaches have been proposed to stream video effectively over packet switched, best-effort networks. We propose a receiver-driven protocol for simultaneous video streaming from multiple senders to a single receiver in order to achieve higher throughput, and to increase tolerance to packet loss and delay due to network congestion. Our receiver-driven protocol employs a novel rate allocation algorithm (RAA) and a packet partition algorithm (PPA). The RAA, run at the receiver, determines the sending rate for each sender by taking into account available network bandwidth, channel characteristics, and a prespecified, fixed level of forward error correction, in such a way as to minimize the probability of packet loss. The PPA, run at the senders based on a set of parameters estimated by the receiver, ensures that every packet is sent by one and only one sender, and at the same time, minimizes the startup delay. Using both simulations and Internet experiments, we demonstrate the effectiveness of our protocol in reducing packet loss.
15918536
Protocols for distributed video streaming
With the explosive growth of video applications over the packet switched networks, many approaches have been proposed to stream video effectively over packet switched, best-effort networks. In our previous work, we proposed a framework with a receiver driven protocol to coordinate simultaneous video streaming from multiple senders to a single receiver in order to achieve higher throughput, and to increase tolerance to packet loss and delay due to network congestion. The receiver-driven protocol employs two algorithms: the rate allocation and packet partition. The rate allocation algorithm determines the sending rate for each sender while the packet partition algorithm ensures no sender sends the same packets, and at the same time, minimizes the probability of late packets. We extend the rate allocation scheme to be used with forward error correction (FEC) in order to minimize the probability of packet loss in a bursty loss environment such as one due to network congestion. Using both simulations and actual Internet experiments, we demonstrate the effectiveness of our rate allocation scheme in reducing packet loss, and hence, achieving higher visual quality for the streamed video.