The PHEME Veracity Intelligence Framework Sample Clauses

The PHEME Veracity Intelligence Framework. Scalability and Efficiency 17
Sample 1
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The PHEME Veracity Intelligence Framework. Scalability and Efficiency‌ The PHEME veracity intelligence framework needs to combine software components related to different tasks (e.g. linguistic pre-processing, veracity analysis, rumour detection) in a flexible fashion. Based on ATOS’s experience from FIRST and ONTO’s from TRENDMINER, a Service Oriented Architecture (SOA) approach will be adopted. Since PHEME will manage large volumes of content, scalability and on-demand compute power will be achieved through cloud computing. Traditionally, the Cloud Computing approach consists of three architectural layers: (i) Software-as- a-Service (SaaS): the software structure (components, interfaces, workflows), based on SOA principles; (ii) Platform-as-a-Service (PaaS): the environment configuration required for the execution of SaaS; (iii) Infrastructure-as-a-Service (IaaS): describes all the physical resources and their characteristics. In the PHEME framework (WP6), the concept will also need to be extended to Database-as-a-Service or Storage-as-a-Service . PHEME will also rely on MapReduce to ensure scalable text processing on large historical data. MapReduce was originally introduced to support large-scale indexing tasks. Of particular relevance to PHEME is the Hadoop Distributed File System (HDFS) - a simple and efficient model for incremental indexing in digital document repositories. Hadoop is already widely used within research, to index sizeable collections such as the Terabyte TREC or the MAREC collection. Finally, PHEME will build on the Storm framework and its adoption within the TRENDMINER project, to provide support for real-time content processing. Xxxxx provides a set of general primitives for distributed real-time computation, e.g. processing messages and updating databases in real-time. Storm can be used for continuous computation (executing a continuous query on data streams and streaming out the results) and/or distributed RPC (running expensive computations in parallel on the fly).
Sample 1
The PHEME Veracity Intelligence Framework. High Level Diagram The basic architecture of the PHEME veracity intelligence framework is based on common SOA principles. The fundamental part is the logical view that should allow a modular and highly generic structure. For this, a three-tier approach has been chosen. This means that the system is decomposed into three different layers which correspond to the following functional blocks: the application, the services, and the persistence of the system. The Application Layer is the application provided to the end users. According the different use cases defined in the project (WP7 and WP8), different applications can be built to provide adapted user interfaces according to the specific requirements. The Application Layer should deal with the configuration of the user interface, and the management of the user interaction to dispatch the events towards the internal system (Service Layer). A very generic approach will be to allow the easy implementation of new applications adapted for specific business processes. The Service Layer is the core of the system. It contains all the main services provided by the system. Those services are called Core Services and as they could be numerous and very different, they are grouped by Service Categories. Those services are atomic functions that could be called whenever by the system. They will be specified by means of Service Component Architecture (SCA) specification and deployed through the runtime Apache Tuscany. The last layer is dedicated to system and data persistency, which is why it is called the Persistence Layer. It has in charge the mechanisms and models to store information. For each kind of information, a repository is required to store the data. Each repository should provide a basic API to describe its own CRUD (Create, Read, Update and Delete) functionalities to permit easy access to the data. After the first results of integration, a generic approach would be studied to define a standard API for services connexion to the PHEME platform. In PHEME, the most important repository is related to the storage of knowledge. As the formalism to represent the knowledge in PHEME is based on RDF, RDF repositories will have an important place in the architecture. The various multilingual content analytics services from WP2, WP3 and WP4 will be included in this Layer. The integration will be designed and tested for on-demand scalability, using a distributed computing based on MapReduce and the Hadoop framework for batch...
Sample 1

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