Description
Publish/Subscribe implements interface and functionality supporting context data acquisition from context sources or providers by application, services or end-users
Functionality
It also allows to the context providers to be registered in the systems with their specific context information and entities they're serving. then any "external" entity needed certain or all available context of a certain entity can obtain required information by requesting or subscribing to the instantiated Publish/Subscribe GE instance. The context information may be requested via two distinct APIs: RESTlike ContextML/CQL supporting very rich set or the requests and subscriptions or by RESTful FI-WARE NGSI based on OMA standard and supporting, for the moment, limited set of supported functionalities.
Use in services and applications
Context information can be provided by many different entities in a independent way respecting to the context consumers. Therefore consumers may not know which is where context information is available. The Publish/Subscribe GE provides the mean to register the context providers and make them available for interrogation by the context consumers (applications, services or end-user). Context consumers can efficiently and simply retriever the context information asking to the Publish/Subscribe GE by a simple request when the context is required in real-time (near real-time) or by subscribing to the Publish/Subscribe for the context information matching certain conditions. This mechanism decouple the context producers and context consumers and allows to the context producers to be registered on-fly while the consumers may benefit from different ways and protocols to retrieve needed context information (position, social status, motion, temperature, etc.). However the context information itself is not within scope of this GE and shall be provided by specific context providers registered in the Publish/Subscribe GE.
Advantages
There is no widely adopted technology which provides a standard API for accessing Context Information. The major goal of the Context Broker GE is to cover that gap providing a very simple yet powerful API that can be adopted as a standard. This would represent a major step beyond in development of smart and context-aware applications. The Context Awareness Platform (CAP) is TI’s implementation of the Context Broker GE. It is not a product itself but a service which enables entities (such as network operators or service providers) to gather and expose context information from heterogeneous sources through as much as possible standard interfaces. This GEi exposes a standard REST-full NGSI interface based on OMA NGSI derived from the TI’s solution years ago. However, CAP has not been offered as a product or
service-to-sell by TI so far. Thus formally no analysis, as a product vs. other products has been performed yet. TI CAP implements a Context Cache to speed up the context retrieval and discharge the overall system. It also keeps History of Context Information which can be used for other purposes, e.g., reasoning and inference. Last but not least, CAP supports interaction using ContextML and CQL (Context Querly Langage) as alternative to the NGSI Restful API.
2.3
Publish/Subscribe Broker - Orion Context Broker
Description
The Orion Context Broker is an implementation of the Publish/Subscribe Context Broker GE, providing the NGSI9 and NGSI10 interfaces. Using these interfaces, clients can do several operations: (a) Register context producer applications, e.g. a temperature sensor within a room; (b) Update context information, e.g. send updates of temperature; (c) Being notified when changes on context information take place (e.g. the temperature has changed) or with a given frequency (e.g. get the temperature each minute); (d) Query context information. The Orion Context Broker stores context information updated from applications, so queries are resolved based on that information.
Functionality
The Context Broker is a GE of the FI-WARE platform that exposes the (standard) interfaces for retrieval of the context information, events and other data from the Context or Data/Event Producers to the Context or Data/Event Consumers. The consumer doesn’t need to know where the data are located and what is the native protocol for their retrieval. It will just communicate to the Context Broker GE through a well-defined interface specifying the data it needed in a defined way: on request or on subscription basis. The Context Broker GE will provide the data back to the consumer when queried, in case of "on-request", or when available, in case of "on-subscription" communication mode.
Use in services and applications
If you are developing a Data/Context scenario, a broker like the Orion Context Broker is a must. You would need a component in the architecture able to mediate between consumer producers (e.g. sensors) and the context consumer applications (e.g. an smartphone applications taking advantage of the context information provided by the sensors). The Orion Context Broker fulfils this functionality in your architecture.
Advantages
There is no widely adopted technology which provides a standard API for accessing Context Information. The major goal of the Context Broker GE is to cover that gap providing a very simple yet powerful API that can be adopted as a standard. This would represent a major step beyond in development of smart and context-aware applications. The Orion Context Broker is
Telefonica’s open source implementation of the Context Broker GE. It relies on MongoDB therefore making it feasible to manage Context Information at a very large scale. Its design is also focused on providing the best performance.
2.4
Big Data Analysis - COSMOS
Description
The BigData Analysis GE is made up from several components: (a) Hadoop as the MapReduce engine for batch processing; (b) HDFS as the distributed file system to store the input, intermediate and eventually output data; (c) MongoDB as the NoSQL database to place the output data for its consumption; (d) Apache Flume, SFTP server and Telefónica's streamConnector as the collection of stream injectors; (e) HUE as the frontend for using and operating Cosmos.
Functionality
The BigData Analysis GE is mainly operated through a set of interfaces around HUE: (a) A web interface is available for creation, monitoring, stop and run of individual or all services in the BigData Analysis GE, scheduling and configuration (workflow design, scheduling, parameterization, etc.). (b) HUE Shell app is the command-line-based counterpart of the web interface. (c) The Filebrowser app allows viewing the results of the MapReduce jobs.
Use in services and applications
The Big Data Analysis Support GE offers a continuous solution for both Big Data Crunching and Big Data Streaming. A key characteristic of this GE is that it would present a unified set of tools and APIs allowing developers to program the analysis on large amount of data and extract relevant insights in both scenarios using a standard programming paradigm (Map&Reduce). Using these APIs, developers will be able to program Intelligent Services such as Social Networks analysis, real-time recommendations, etc. These Intelligent Services will be plugged in the Big Data Analysis GE using a number of tools and APIs that this GE will support.
Advantages
(a) The streaming and batch processing functionalities both in one single platform. Due to batch and stream processing are managed by using totally different approaches, today Big Data platforms are uniquely oriented to a unique type of data: large log files or continuous streams of data. The envisioned GE will be able to deal with both, firstly by allowing injectors for streams that will be internally turn into batches in order to perform MapReduce techniques (first releases), and then by performing real differentiated batch and streaming processing (final releases). (b) The automatic deployment capabilities in a cloud-based cluster of nodes. Big Data platforms are designed to deploy on a cluster of commodity hardware. This GE goes far beyond and proposes replace the physical machines by virtual nodes and provides means to automatically deploy on such a cloud-based cluster. (c) The wide range of available
data injectors. The GE will expose a set of interfaces ready to accept data in several formats and ways, e.g. the above mentioned stream injectors, but also agent-based gatherers of data and conventional file transfer systems. (d) The high speed access to the resulting insights via a NoSQL database. Today Big Data platforms relay on distributed file systems to store the input data and all its intermediate transformations since it is the unique way to manage large files manipulation. Nevertheless, the throughput of these distributed file systems is not high, which becomes especially critical when accessing several times to the same piece of data: the results. Thus, the BigData GE foresees to use a NoSQL database where to copy the resulting insights and access them with high throughput rates.
2.5
Compressed Domain Video Analysis – Codoan
Description
The target users of the Compressed Domain Video Analysis GE are all applications that want to extract meaningful information from video content and that need to automatically find characteristics in video streams on given tasks. The GE can work for previously stored video data as well as for video data streams (e.g., received from a camera in real time)
Functionality
A realization of the Compressed Domain Video Analysis GE consists of a set of tools for analyzing video streams in the compressed domain. Its purpose is to avoid costly video content decoding prior to the actual analysis. Thereby, the tool set processes video streams by analyzing compressed or just partially decoded syntax elements. The main benefit is its very fast analysis due to a hierarchical architecture
Use in services and applications
(a) Critical product attributes for the Compressed Domain Video Analysis GE are especially high detection/recognition ratios containing only few false positives and low-complexity operation; (b) Partitioning to independent functional blocks enables the GE to support a variety of analysis methods and to get easily extended by new features. Even several operations can be combined; (c) Low-complexity algorithms and implementations enable the GE to perform very fast analyses and to be highly scalable (d) GE implementations support performing parallel analyses using different sub-components
Advantages
Moving object detection is probably one of the most widely used video analysis procedures in many different applications, e.g., in the security domain but also for patience care. Video surveillance systems need to detect moving persons or vehicles, trackers have to be initialized
with the objects they should track, and recognition algorithms require the regions within the scene where they should identify objects. For this reason, several components/systems for efficient object detection have been released and are offered in the security market. Most of them operate in the pixel domain, i.e., on the actual pixel data of each frame. This usually leads to a very high accuracy, but at the expense of computational complexity. As most video data is stored or transferred in compressed representation, the bit stream has to be completely decoded beforehand in such scenarios. Therefore, the Compressed Domain Video Analysis GE makes the attempt to eliminate the costly step of decoding and to perform the analysis directly in the compressed domain. Compared to currently deployed systems, this gives significant advantages in terms of computational complexity and therefore also offers cost savings especially for large-scale analytics systems.
2.6
Media-enhanced Query Broker – Query Broker
Description
The Media-enhanced Query Broker GE provides an intelligent, abstracting interface for retrieval of data from distributed and heterogeneous data resources. Principal users of the Media-enhanced Query Broker GE include applications that require a selective, on-demand view on various data repositories via a single, unified API, without taking care about the specifics of the internal data storage and DB implementations and interfaces. Therefore, this GE provides support for integration of query functions into the users’ applications by abstracting the access to databases and search engine. At the same time its API offers an abstraction from the distributed and heterogeneous nature of the underlying storage, retrieval and DB / metadata schema implementations.
Functionality
The QueryBroker is implemented as a middleware to establish unified retrieval in distributed and heterogeneous environments with extension functionalities to integrate multimedia specific retrieval paradigms in the overall query execution plan, e.g., multimedia fusion technique. To ensure interoperability between the query applications and the registered database services, the QueryBroker uses as internal query representation format the MPEG Query Format (MPQF). MPQF is an XML-based (multimedia) query language which defines the format of queries and replies to be interchanged between clients and servers in a (multimedia) information search and retrieval environment.
Use in services and applications
(a) Middleware component for unified access to distributed and heterogeneous data repositories (with extensions supporting multimedia repositories); (b) Abstraction from heterogeneous retrieval paradigms in the underlying data bases and search engines; (c) Loosely coupled, modular architecture (easy extensibility)
Advantages
Today data - and especially in the media domain - is produced at an immense rate. By investigating solutions and approaches for storing and archiving the produced data, one rapidly ends up in a highly heterogeneous environment of data stores. Usually, the involved domains feature individual sets of metadata formats and the data sets are accessible in different systems supporting a multiple set of retrieval models and query languages. Thus an easy and efficient access and retrieval across those system borders is a very cumbersome task. In the last few years, several approaches for accessing multi-media data in a possibly distributed and heterogeneous environment have been proposed, but those systems are mainly dedicated to certain domains (e.g. medical) supporting only corresponding metadata formats (e.g. DICOM) and often are not able to address heterogeneous data sources. Furthermore these systems lack in the expressiveness of multi-media queries and metadata interoperability. In contrast to the existing approaches the media-enhanced Query Borker provides a unified search interface for heterogeneous and distributed data stores with a particular focus on integrating multimedia data in the query and retrieval processes. To ensure interoperability between the query applications and the registered database services, the Media-enhanced Query Broker makes use of the standardized MPEG Query Format, which provides a standardized interface to (multi-)media repositories, as well to metadata modeled with Semantic Web languages like RDF and the Web Ontology Language, and query constructs based on SPARQL.
2.7
Location LOCS
Description
The Location Server (LOCS) is a Thales Alenia Space France (TAS-F) platform dedicated to location management of wireless devices (2G, 2.5G, 3G, 4G). This platform is based on various positioning techniques such as A-GPS, WiFi and Cell-Id activated with intelligence whilst taking into account the end-user privacy.
Functionality
The Location GE in FI-WARE targets any third-party application that aims to retrieve mobile device positions and area events. The Location GE is based on various positioning techniques such as A-GPS, Wi-Fi and Cell-Id intelligently triggered whilst taking into account the end-user privacy. This GE addresses issues related to Location of mobile devices in difficult environments such as urban canyons and light indoor environments where the GPS receiver in the mobile device is not able to acquire weak GPS signals without assistance. In more difficult conditions like deep indoor, the Location GE selects other positioning techniques like Wi-Fi to locate the end-user. It therefore improves localization yield, which enhances the end-user experience and the performance of applications requesting the position of mobile devices.
Use in services and applications
Use this platform if you need to retrieve the location of IP connected devices (simulated in the test bed) using various location methods delivering different quality of service: A-GPS for very accurate positioning but slow response time in outdoor environment, WiFi for fairly accurate positioning and fast response time in indoor environment and Cell-Id for rough location but fast response time in every kind of environment. Experience the various applications available via the restful interface provided, such as location retrieval (R1), geo-fencing (R2) and dynamic selection of the location method based on the end-user environment (R2).
Advantages
The Location GE implements the very latest protocol standards such as SUPLv2 whereas competition currently relies on SUPLv1. SUPLv2 brings many new features like periodic tracking and geofencing features which are not possible with the previous version of the standard. Moreover, the Location GE provides a RESTFul API for accessing mobile device positions, whereas the competition relies on more complicated interfaces based on HTTP like OMA MLP. The core functionality of the Location GE is AGNSS technology which has been developed by a team of GNSS experts (TAS is system prime of EGNOS and prime of Galileo Mission Segment), bringing to the product advanced GNSS algorithms.
2.8
Semantic Application Support
Description
The main goal of the Semantic Web Application Enabler is to provide a framework for ontology engineers and developers of semantically-enabled applications offering RDF/OWL management, storage and retrieval capabilities. This goal will be achieved by providing an infrastructure for metadata publication, retrieval and subscription that meets industry requirements like scalability, distribution and security, plus a set of tools for infrastructure and metadata-data management, supporting most adopted methodologies and best practices.
Functionality
The Semantic Web Application enabler is based on the following design principles: (a) Support standards: Support for RDF/OWL, the most common standards used in Semantic Web applications. (b) Methodological approach: GE is strongly influenced by methodological approaches, so it will adopt and support, as far as possible, most adopted methodologies to achieve its goals. (c) Semantic repository features: Provide high-level common features valid for most of the existing solutions in the semantic web in terms of RDF / OWL storage and inference functionalities. (d) Ontology management: The enabler will provide an ontology registry and the API to control it, including some high-level ontology management functionalities. (e) Knowledge Base management: The enabler will provide a knowled base registry and the API to control it, including some high level knowledge base management functionalities. (f) Extensibility: The most important part of the architecture design of the
enabler is to define interfaces that allow the extensibility of the system. Where applicable the design should also be modular, to facilitate future extensions and improvements. The reference implementations should comply with this common design.
Use in services and applications
Semantic Web applications skateholders will benefit from this generic enabler that: (a) Provides an infrastructure for semantic web applications that support large scale applications including: metadata storage in RDF, publication of RDF triples, querying by SPARQL and inference. (b) Provides a framework for supporting methodologies and engineering processes related with metadata management and ontology development.
Advantages
None
2.9
SemanticAnnotation- SANr
Description
Semantic Annotator GE performs named entity recognition and semantically links them with Linked Open Data objects. Named Entity Recognition can recognize persons, places and organizations in a text. Once recognized, each entity is passed to a semantic broker, who tries to identify the correct correspondence over the most used linked open data repositories (dbpedia, which is general, and geonames, for places). It can also provide html snippets describing content for dbpedia entries.
Functionality
Semantic Annotation GE aims at performing named entity recognition and semantic annotation for a given text. The basic Idea is to use an open-source language processor (Freeling) plus some custom software to identifies the entities contained in the text to analyze. Once the entities (which are basically persons, places and organizations) are identified, the system searches into semantic triple stores and databases RDF information about those entities by means of SPARQL Queries. For each entity the system offers the set of candidates found (if existing) each one with a related “score” giving an hint of the one who