Supporting Semantically Enhanced Web Service Discovery for Enterprise Application Integration

The availability of sophisticated Web service discovery mechanisms is an essential prerequisite for increasing the levels of efficiency and automation in EAI. In this chapter, we present an approach for developing service registries building on the UDDI standard and offering semantically-enhanced publication and discovery capabilities in order to overcome some of the known limitations of conventional service registries. The approach aspires to promote efficiency in EAI in a number of ways, but primarily by automating the task of evaluating service integrability on the basis of the input and output messages that are defined in the Web service’s interface. The presented solution combines the use of three technology standards to meet its objectives: OWL-DL, for modelling service characteristics and performing fine-grained service matchmaking via DL reasoning, SAWSDL, for creating semantically annotated descriptions of service interfaces, and UDDI, for storing and retrieving syntactic and semantic information about services and service providers

XML Matchers: approaches and challenges

Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, etc.). However, in the latest years, the widespread adoption of XML in the most disparate application fields pushed a growing number of researchers to design XML-specific Schema Matching approaches, called XML Matchers, aiming at finding semantic matchings between concepts defined in DTDs and XSDs. XML Matchers do not just take well-known techniques originally designed for other data models and apply them on DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical structure of a DTD/XSD) to improve the performance of the Schema Matching process. The design of XML Matchers is currently a well-established research area. The main goal of this paper is to provide a detailed description and classification of XML Matchers. We first describe to what extent the specificities of DTDs/XSDs impact on the Schema Matching task. Then we introduce a template, called XML Matcher Template, that describes the main components of an XML Matcher, their role and behavior. We illustrate how each of these components has been implemented in some popular XML Matchers. We consider our XML Matcher Template as the baseline for objectively comparing approaches that, at first glance, might appear as unrelated. The introduction of this template can be useful in the design of future XML Matchers. Finally, we analyze commercial tools implementing XML Matchers and introduce two challenging issues strictly related to this topic, namely XML source clustering and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure

Combining SAWSDL, OWL-DL and UDDI for Semantically Enhanced Web Service Discovery

UDDI registries are included as a standard offering within the product suite of any major SOA vendor, serving as the foundation for establishing design-time and run-time SOA governance. Despite the success of the UDDI specification and its rapid uptake by the industry, the capabilities of its offered service discovery facilities are rather limited. The lack of machine-understandable semantics in the technical specifications and classification schemes used for retrieving services, prevent UDDI registries from supporting fully automated and thus truly effective service discovery. This paper presents the implementation of a semantically-enhanced registry that builds on the UDDI specification and augments its service publication and discovery facilities to overcome the aforementioned limitations. The proposed solution combines the use of SAWSDL for creating semantically annotated descriptions of service interfaces and the use of OWL-DL for modelling service capabilities and for performing matchmaking via DL reasoning

Automatic Transformation of Relational Database Schema into OWL Ontologies

Ontology alignment, or ontology matching, is a technique to map different concepts between ontologies. For this purpose at least two ontologies are required. In certain scenarios, such as data integration, heterogeneous database integration and data model compatibility evaluation, a need to transform a relational database schema to an ontology can arise. To conduct a successful transformation it is necessary to identify the differences between relational database schema and ontology information representation methods, and then to define transformation rules. The most straight forward but time consuming way to carry out transformation is to do it manually. Often this is not an option due to the size of data to be transformed. For this reason there is a need for an automated solution.The automatic transformation of OWL ontology from relational database schema is presented in this paper; the data representation differences between relational database schema and OWL ontologies are described; the transformation rules are defined and the transformation tool’s prototype is developed to perform the described transformation

Profiling relational data: a survey

Profiling data to determine metadata about a given dataset is an important and frequent activity of any IT professional and researcher and is necessary for various use-cases. It encompasses a vast array of methods to examine datasets and produce metadata. Among the simpler results are statistics, such as the number of null values and distinct values in a column, its data type, or the most frequent patterns of its data values. Metadata that are more difficult to compute involve multiple columns, namely correlations, unique column combinations, functional dependencies, and inclusion dependencies. Further techniques detect conditional properties of the dataset at hand. This survey provides a classification of data profiling tasks and comprehensively reviews the state of the art for each class. In addition, we review data profiling tools and systems from research and industry. We conclude with an outlook on the future of data profiling beyond traditional profiling tasks and beyond relational databases

Extracting Role-Based Access Control Models from Business Process Event Logs

Keeruliste äriprotsesside ja järjest suurenevate andmemahtude juures on väljakutsuvaks ülesandeks analüüsida ja parandada ettevõtte äriprotsessi andmeturvalisust. Infosüsteemid, mis toetavad äriprotsessi mudeli (abstraktne esitus äriprotsessist) rakendamist, registreerivad äriprotsessi tegevusi sündmustena eraldi logisse. Salvestatud sündmuste logid on aluseks äriprotsessiga seotud andmete kaevamiseks. Need andmed on vajalikud äriprotsessi analüüsimiseks ja parendamiseks, kuid neid andmeid võib kasutada ka turvaanalüüsiks. Turvaanalüüsi üheks eesmärgiks on ka kontrollida, kas nende andmete hulgas turvalisusega seotud informatsioon on kooskõlas praeguste turvanõuetega. Lisaks, äriprotsessi logide peal saab rakendada äriprotsessikaeve (uurimisvaldkond, mis ühendab andmekaeve ja äriprotsesside modelleerimise) tehnikaid, et luua äriprotsessi mudeleid. Lisaks äriprotsessi mudelitele on võimalik tuletada ka teisi mudeleid, näiteks turvamudeleid, mida saab hiljem kasutada turvameetmete tagamiseks infosüsteemis. Käesoleva töö eesmärgiks on esitada üks võimalik meetod, kuidas luua rollipõhist ligipääsukontrolli esitatavaid turvamudeleid (Role- Based Access Control models) XES-formaadis sündmuste logidest, mis on salvestatud äriprotsessi toetava infosüsteemi poolt. Lisatähelepanu on suunatud kaitstavate infovarade väljaselgitamiseks sündmuste logide põhjal. Need infovarad on näiteks dokumendid, dokumendiväljad, või muud andmed, mida töödeldakse äriprotsessi tegevuste jooksul. Lisaks, me hindame antud meetodi rakendatavust reaalse äriprotsessi sündmuste logi peal. Ühe võimaliku meetodina me kontrollime sündmuste logi andmete ja seoste vastavust juurdepääsu õigustega olemasoleva rollipõhise juurdepääsu kontrolli turvamudelis. Lõppkokkuvõttes võib sündmuste logidest tuletatud rollipõhist ligipääsu kontrolli mudelit võtta aluseks turvaanalüüsiks või rakendada mõnes süsteemis juurdepääsumehhanismina.Today, as business processes are getting more complex and the volumes of stored data about business process executions are increasing in size, collecting information for the analysis and for the improvement of the business process security1, is becoming a complex task. Information systems that support business processes record business process executions into event logs which capture the behavior of system usage in terms of events. Business process event logs can be used for analysing and improving the business process, but also for analysing the information security. One of the main goals of security analysis is to check the compliance with existing security requirements. Also event logs can be the basis for business process mining, or shortly process mining. Utilizing bottom-up process mining on event logs, we can extract business process-related information for security analysis. Process mining is not just only for discovering business process models, but also other models, such as security models. For this purpose, we present a possible approach to extract RBAC models (semi-)automatically from event logs in XES format. The focus is also on determining the protected business assets, such as document or other artifact data that is exchanged and accessed during business process activities. In addition, we evaluate the applicability of this approach with conformance checking where we check the compliance of a real-life event log with respect to the LTL constraints translated from RBAC model. Eventually, the purpose of the extracted RBAC models is that they provide a basis for security analysis and they can be adapted by other applications in order to implement access control mechanism

EdgeBOX remote management

Tese de mestrado integrado. Engenharia Informátca e Computação. Faculdade de Engenharia. Universidade do Porto. 200