Service Semantics Classification: an Approach Towards Modular Service Ontology

Since service systems are becoming increasingly complex in emerging technology, business, legal and economics environments, service abstractions are necessary to master this complexity. However, the term ‘service’ means different things to different people in different disciplines, which implies that any attempt to define general purpose service abstractions must address the disambiguation of the term. Service ontologies and service knowledge management efforts mainly aim at elucidating service semantics. Each discipline has multiple biased service-related concepts, so that in order to build comprehensive multi-disciplinary service models, the service-related concepts of the involved disciplines have to be integrated and structured in a consistent way. We claim that this requires a modular approach in which general purpose service semantics can be further extended or specialised with domain-specific concepts. Service-related and domain-specific concepts can be integrated and structured in many different ways. This paper proposes a semantics classification scheme based on service aspects that are essential for a services ecosystem

Modern middleware for the data acquisition of the Cherenkov Telescope Array

The data acquisition system (DAQ) of the future Cherenkov Telescope Array (CTA) must be ef- ficient, modular and robust to be able to cope with the very large data rate of up to 550 Gbps coming from many telescopes with different characteristics. The use of modern middleware, namely ZeroMQ and Protocol Buffers, can help to achieve these goals while keeping the development effort to a reasonable level. Protocol Buffers are used as an on-line data for- mat, while ZeroMQ is employed to communicate between processes. The DAQ will be controlled and monitored by the Alma Common Software (ACS). Protocol Buffers from Google are a way to define high-level data structures through an in- terface description language (IDL) and a meta-compiler. ZeroMQ is a middleware that augments the capabilities of TCP/IP sockets. It does not implement very high-level features like those found in CORBA for example, but makes use of sockets easier, more robust and almost as effective as raw TCP. The use of these two middlewares enabled us to rapidly develop a robust prototype of the DAQ including data persistence to compressed FITS files.Comment: In Proceedings of the 34th International Cosmic Ray Conference (ICRC2015), The Hague, The Netherlands. All CTA contributions at arXiv:1508.0589

VOX : an extensible natural language processor

VOX is a Natural Language Processor whose knowledge can be extended by interaction with a user.VOX consists of a text analyzer and an extensibility system that share a knowledge base. The extensibility system lets the user add vocabulary, concepts, phrases, events, and scenarios to the knowledge base. The analyzer uses information obtained in this way to understand previously unhandled text.The underlying knowledge representation of VOX, called Conceptual Grammar, has been developed to meet the severe requirements of extensibility. Conceptual Grammar uniformly represents syntactic and semantic information, and permits modular addition of knowledge

The Structured Process Modeling Theory (SPMT): a cognitive view on why and how modelers benefit from structuring the process of process modeling

After observing various inexperienced modelers constructing a business process model based on the same textual case description, it was noted that great differences existed in the quality of the produced models. The impression arose that certain quality issues originated from cognitive failures during the modeling process. Therefore, we developed an explanatory theory that describes the cognitive mechanisms that affect effectiveness and efficiency of process model construction: the Structured Process Modeling Theory (SPMT). This theory states that modeling accuracy and speed are higher when the modeler adopts an (i) individually fitting (ii) structured (iii) serialized process modeling approach. The SPMT is evaluated against six theory quality criteria

Wireless and Physical Security via Embedded Sensor Networks

Wireless Intrusion Detection Systems (WIDS) monitor 802.11 wireless frames (Layer-2) in an attempt to detect misuse. What distinguishes a WIDS from a traditional Network IDS is the ability to utilize the broadcast nature of the medium to reconstruct the physical location of the offending party, as opposed to its possibly spoofed (MAC addresses) identity in cyber space. Traditional Wireless Network Security Systems are still heavily anchored in the digital plane of "cyber space" and hence cannot be used reliably or effectively to derive the physical identity of an intruder in order to prevent further malicious wireless broadcasts, for example by escorting an intruder off the premises based on physical evidence. In this paper, we argue that Embedded Sensor Networks could be used effectively to bridge the gap between digital and physical security planes, and thus could be leveraged to provide reciprocal benefit to surveillance and security tasks on both planes. Toward that end, we present our recent experience integrating wireless networking security services into the SNBENCH (Sensor Network workBench). The SNBENCH provides an extensible framework that enables the rapid development and automated deployment of Sensor Network applications on a shared, embedded sensing and actuation infrastructure. The SNBENCH's extensible architecture allows an engineer to quickly integrate new sensing and response capabilities into the SNBENCH framework, while high-level languages and compilers allow novice SN programmers to compose SN service logic, unaware of the lower-level implementation details of tools on which their services rely. In this paper we convey the simplicity of the service composition through concrete examples that illustrate the power and potential of Wireless Security Services that span both the physical and digital plane.National Science Foundation (CISE/CSR 0720604, ENG/EFRI 0735974, CIES/CNS 0520166, CNS/ITR 0205294, CISE/ERA RI 0202067