Systems and methods are described for allowing the execution of authorized computer program code and for protecting computer systems and networks from unauthorized code execution. In one embodiment, a multi-level proactive whitelist approach is employed to secure a computer system by allowing only the execution of authorized computer program code thereby protecting the computer system against the execution of malicious code such as viruses, Trojan horses, spy-ware, and/or the like. Various embodiments use a kernel-level driver, which intercepts or “hooks” certain system Application Programming Interface (API) calls in order to monitor the creation of processes prior to code execution. The kernel-level driver may also intercept and monitor the loading of code modules by running processes, and the passing of non-executable code modules, such as script files, to approved or running code modules via command line options, for example. Once intercepted, a multi-level whitelist approach may be used to authorize the code execution.

Secure system for allowing the execution of authorized computer program code

https://www.kde.cs.uni-kassel.de/hotho/pub/2006/JWS2006SemanticWebMining.pdf

Semantic Web Mining

The different advantageous embodiments may provide an apparatus that may comprise a number of mobile robotic machines, a wireless communications system and a computer system. The number of mobile robotic machines may be capable of moving to a number of locations in a maintenance area and may be capable of performing a number of maintenance operations on a structure in the maintenance area. The wireless communications system may be capable of providing communication with the number of mobile robotic machines within the maintenance area. The computer system may be capable of exchanging information with the number of mobile robotic machines using the wireless communications system.

Autonomous inspection and maintenance

This paper presents an ontology-based approach for web querying, using semantic metadata. We propose a query language based on ontologies and emphasize its ability to express approximate queries, useful for an efficient information retrieval on the web. We present the Corese search engine dedicated to RDF(S) metadata and illustrate it through several real-world applications.

/pdf/querying-the-semantic-web-with-corese-search-engine-46ub9c7bbj.pdf

Querying the Semantic Web with Corese search engine

In this document we provide an overall view of the state of the art in ontology alignment. It is organised as a description of the need for ontology alignment, a presentation of the techniques currently in use for ontology alignment and a presentation of existing systems. The state of the art is not restricted to any discipline and consider as some form of ontology alignment the work made on schema matching within the database area for instance. Keyword list: schema matching, ontology matching, ontology alignment, ontology mapping, schema mapping, Copyright c 2004 The contributors Document Identifier KWEB/2004/D2.2.3/v1.2 Project KWEB EU-IST-2004-507482 Version v1.2 Date August 2, 2004 State final Distribution public Knowledge Web Consortium This document is part of a research project funded by the IST Programme of the Commission of the European Communities as project number IST-2004-507482. University of Innsbruck (UIBK) Coordinator Institute of Computer Science Technikerstrasse 13 A-6020 Innsbruck Austria Contact person: Dieter Fensel E-mail address: dieter.fensel@uibk.ac.at École Polytechnique Fédérale de Lausanne (EPFL) Computer Science Department Swiss Federal Institute of Technology IN (Ecublens), CH-1015 Lausanne Switzerland Contact person: Boi Faltings E-mail address: boi.faltings@epfl.ch France Telecom (FT) 4 Rue du Clos Courtel 35512 Cesson Sévigné France. PO Box 91226 Contact person : Alain Leger E-mail address: alain.leger@rd.francetelecom.com Freie Universität Berlin (FU Berlin) Takustrasse 9 14195 Berlin Germany Contact person: Robert Tolksdorf E-mail address: tolk@inf.fu-berlin.de Free University of Bozen-Bolzano (FUB) Piazza Domenicani 3 39100 Bolzano Italy Contact person: Enrico Franconi E-mail address: franconi@inf.unibz.it Institut National de Recherche en Informatique et en Automatique (INRIA) ZIRST 655 avenue de l’Europe Montbonnot Saint Martin 38334 Saint-Ismier France Contact person: Jérôme Euzenat E-mail address: Jerome.Euzenat@inrialpes.fr Centre for Research and Technology Hellas / Informatics and Telematics Institute (ITI-CERTH) 1st km Thermi Panorama road 57001 Thermi-Thessaloniki Greece. Po Box 361 Contact person: Michael G. Strintzis E-mail address: strintzi@iti.gr Learning Lab Lower Saxony (L3S) Expo Plaza 1 30539 Hannover Germany Contact person: Wolfgang Nejdl E-mail address: nejdl@learninglab.de National University of Ireland Galway (NUIG) National University of Ireland Science and Technology Building University Road Galway Ireland Contact person: Christoph Bussler E-mail address: chris.bussler@deri.ie The Open University (OU) Knowledge Media Institute The Open University Milton Keynes, MK7 6AA United Kingdom Contact person: Enrico Motta E-mail address: e.motta@open.ac.uk Universidad Politécnica de Madrid (UPM) Campus de Montegancedo sn 28660 Boadilla del Monte Spain Contact person: Asunción Gómez Pérez E-mail address: asun@fi.upm.es University of Karlsruhe (UKARL) Institut für Angewandte Informatik und Formale Beschreibungsverfahren AIFB Universität Karlsruhe D-76128 Karlsruhe Germany Contact person: Rudi Studer E-mail address: studer@aifb.uni-karlsruhe.de University of Liverpool (UniLiv) Chadwick Building, Peach Street L697ZF Liverpool United Kingdom Contact person: Michael Wooldridge E-mail address: M.J.Wooldridge@csc.liv.ac.uk University of Manchester (UoM) Room 2.32. Kilburn Building, Department of Computer Science, University of Manchester, Oxford Road Manchester, M13 9PL United Kingdom Contact person: Carole Goble E-mail address: carole@cs.man.ac.uk University of Sheffield (USFD) Regent Court, 211 Portobello street S14DP Sheffield United Kingdom Contact person: Hamish Cunningham E-mail address: hamish@dcs.shef.ac.uk University of Trento (UniTn) Via Sommarive 14 38050 Trento Italy Contact person: Fausto Giunchiglia E-mail address: fausto@dit.unitn.it Vrije Universiteit Amsterdam (VUA) De Boelelaan 1081a 1081HV. Amsterdam The Netherlands Contact person: Frank van Harmelen E-mail address: Frank.van.Harmelen@cs.vu.nl Vrije Universiteit Brussel (VUB) Pleinlaan 2, Building G1

State of the art on ontology alignment

With the aim of building a ”Semantic Web”, the content of the documents must be explicitly represented through metadata in order to enable contents-guided search. Our approach is to exploit a standard language (RDF, recommended by W3C) for expressing such metadata and to interpret these metadata in conceptual graphs (CG) in order to exploit querying and inferencing capabilities enabled by CG formalism. The paper presents our mapping of RDF into CG and its interest in the context of the semantic Web.

/pdf/a-conceptual-graph-model-for-w3c-resource-description-z83y8mtwq4.pdf

A Conceptual Graph Model for W3C Resource Description Framework

A system for providing secured access to an application service includes a challenge provider that uses a first cryptographic technique to provide a challenge to a client seeking access to an application service. The client uses a second cryptographic technique to generate a response, and provides the response to an authentication service. The authentication service grants the client access to the application service only if the challenge and response are authenticated using a first authentication technique complementary to the first cryptographic technique and a second authentication technique complementary to the second cryptographic technique, respectively.

Client authentication using a challenge provider

The Big Data revolution promises to build a data-driven ecosystem where better decisions are supported by enhanced analytics and data management. However, major hurdles still need to be overcome on the road that leads to commoditization and wide adoption of Big Data Analytics (BDA). Big Data complexity is the first factor hampering the full potential of BDA. The opacity and variety of Big Data technologies and computations, in fact, make BDA a failure prone and resource-intensive process, which requires a trial-and-error approach. This problem is even exacerbated by the fact that current solutions to Big Data application development take a bottom-up approach, where the last technology release drives application development. Selection of the best Big Data platform, as well as of the best pipeline to execute analytics, represents then a deal breaker. In this paper, we propose a return to roots by defining a Model-Driven Engineering (MDE) methodology that supports automation of BDA based on model specification. Our approach lets customers declare requirements to be achieved by an abstract Big Data platform and smart engines deploy the Big Data pipeline carrying out the analytics on a specific instance of such platform. Driven by customers’ requirements, our methodology is based on an OWL-S ontology of Big Data services and on a compiler transforming OWL-S service compositions in workflows that can be directly executed on the selected platform. The proposal is experimentally evaluated in a real-world scenario focusing on the threat detection system of SAP.

/pdf/model-based-big-data-analytics-as-a-service-take-big-data-to-19y4nwntpj.pdf

Model-Based Big Data Analytics-as-a-Service: Take Big Data to the Next Level

The Big Data revolution has promised to build a data-driven ecosystem where better decisions are supported by enhanced analytics and data management. However, critical issues still need to be solved in the road that leads to commodization of Big Data Analytics, such as the management of Big Data complexity and the protection of data security and privacy. In this paper, we focus on the first issue and propose a methodology based on Model Driven Engineering (MDE) that aims to substantially lower the amount of competences needed in the management of a Big Data pipeline and to support automation of Big Data analytics. The proposal is experimentally evaluated in a real-world scenario: the implementation of novel functionality for Threat Detection Systems.

A Model-Driven Methodology for Big Data Analytics-as-a-Service

Methods and computer systems are provided for workflow management. A workflow engine may be provided that creates workflow items. Each workflow item may include a self descriptive part that specifies details for generating a device independent representations of a corresponding workflow item. Further, a workflow manager may be provided that generates the device independent representation based on the self descriptive part.

Cedric Hebert

Papers

A Conceptual Graph Model for W3C Resource Description Framework

Client authentication using a challenge provider

Model-Based Big Data Analytics-as-a-Service: Take Big Data to the Next Level

A Model-Driven Methodology for Big Data Analytics-as-a-Service

Methods and computer systems for workflow management