An Integration of Rule Types and XML

Meta data types for providing business rules are described. These business rules form part of the knowledge base of an organisation. The integration of knowledge with data and information is discussed. FORM, a natural language based, conceptual modelling technique is used to describe the meta data types. XML has been recognised by many in the industry as the preferred data interchange language. XML is currently being used to interchange data and its schema. The use of XML to interchange rules is being investigated, in this paper. This will enable organisations to interchange business rules and their meta data, in addition to data and their schema. Such business rules can be interpreted and applied by the receiving systems, thus providing a basis for intelligent behavior when dealing with information being interchange

Methods and Applications of Data Mining in Business Domains

This Special Issue invited researchers to contribute original research in the field of data mining, particularly in its application to diverse domains, like healthcare, software development, logistics, and human resources. We were especially interested in how the data mining method was modified to cater to the specific domain in question. The challenge is that the more complex a domain is the harder it is to make good predictions, as more implicit domain knowledge is required that is not always available [1]. This is especially true in the case of complex domains where there are soft factors, like the interaction of the conflicting and cooperating objectives of the stakeholders [2,3], and system dynamics play a significant role [4]. In a business context, the challenge is that one would like to see (i) how the algorithms can be repeatable in the real world, (ii) how the patterns mined can be utilized by the business, and (iii) how the resulting model can be understood and utilized in the business environment [1]. Furthermore, the idea is to identify the variables that impact the goal variable but to do so with the data, interestingness, deployment, and general domain (business) constraints of the domain [1,5]. One of the methods to analyze a complex domain is using a method called intelligence meta-synthesis [6,7]. Intelligence synthesis is the collection and creation of perceived or understood (i.e., not necessarily objective) information. Meta-synthesis is the collection and creation of knowledge and information from collected intelligences [1]. The goal of this approach is to design and develop predictive models that could eventually be incorporated into a business intelligence dashboard. As a result, one would (i) understand the nature and origin of data that allows the system user to determine the quality of the data to perform the data cleaning; (ii) understand the factors in the domain that influence the predicted variable, leading the developer to determine which variables need to be included in the predictive model; (iii) develop predictive models that are usable and interesting within the domain in terms of predictive power, integrating with existing infrastructure, and integrating with business rules and processes; and finally (iv) use the predicted data to find the optimal business processes in the particular domain. There are also research works that have built on top of intelligence meta-synthesis, such as the study published by the authors of [1]

Expanding the Data Warehouse Paradigm to Support the Virtual Company

Virtual Organizations (VO) are on the rise fueled by a more global base for commerce, technology development to support VOs and a more global demand for products, and virtuality can fundamentally transform the ways in which organizations operate. In order to operate successfully a VO must develop a knowledge infrastructure which allows for seamless flow of information between geographically distributed people, processes and repositories. To support this a virtual data warehouse needs an architectural design with a logically common meta-data, common semantics and common business rules. While data warehouses have been designed to support the traditional organization the VO’s high level of uncertainty and lack of centralized control make the construction of a data warehouse to support a VO more challenging. This paper presents two possible scenarios for accomplishing this along with advantages and disadvantages of both and measures of success

Enterprise information integration: on discovering links using genetic programming

Both established and emergent business rely heavily on data, chiefly those that wish to become game changers. The current biggest source of data is the Web, where there is a large amount of sparse data. The Web of Data aims at providing a unified view of these islands of data. To realise this vision, it is required that the resources in different data sources that refer to the same real-world entities must be linked, which is they key factor for such a unified view. Link discovery is a trending task that aims at finding link rules that specify whether these links must be established or not. Currently there are many proposals in the literature to produce these links, especially based on meta-heuristics. Unfortunately, creating proposals based on meta-heuristics is not a trivial task, which has led to a lack of comparison between some well-established proposals. On the other hand, it has been proved that these link rules fall short in cases in which resources that refer to different real-world entities are very similar or vice versa. In this dissertation, we introduce several proposals to address the previous lacks in the literature. On the one hand we, introduce Eva4LD, which is a generic framework to build genetic programming proposals for link discovery; which are a kind of meta-heuristics proposals. Furthermore, our framework allows to implement many proposals in the literature and compare their results fairly. On the other hand, we introduce Teide, which applies effectively the link rules increasing significantly their precision without dropping their recall significantly. Unfortunately, Teide does not learn link rules, and applying all the provided link rules is computationally expensive. Due to this reason we introduce Sorbas, which learns what we call contextual link rules.Las empresas que desean establecer un precedente en el panorama actual tienden a recurrir al uso de datos para mejorar sus modelos de negocio. La mayor fuente de datos disponible es la Web, donde una gran cantidad es accesible aunque se encuentre fragmentada en islas de datos. La Web de los Datos tiene como objetivo dar una visión unificada de dichas islas, aunque el almacenamiento de los mismos siga siendo distribuido. Para ofrecer esta visión es necesario enlazar los recursos presentes en las islas de datos que hacen referencia a las mismas entidades del mundo real. Link discovery es el nombre atribuido a esta tarea, la cual se basa en generar reglas de enlazado que permiten establecer bajo qué circunstancias dos recursos deben ser enlazados. Se pueden encontrar diferentes propuestas en la literatura de link discovery, especialmente basadas en meta-heurísticas. Por desgracia comparar propuestas basadas en meta-heurísticas no es trivial. Por otro lado, se ha probado que estas reglas de enlazado no funcionan bien cuando los recursos que hacen referencia a dos entidades distintas del mundo real son muy parecidos, o por el contrario, cuando dos recursos muy distintos hacen referencia a la misma entidad. En esta tesis presentamos varias propuestas. Por un lado, Eva4LD es un framework genérico para desarrollar propuestas de link discovery basadas en programación genética, que es un tipo de meta-heurística. Gracias a nuestro framework, hemos podido implementar distintas propuestas de la literatura y comprar justamente sus resultados. Por otro lado, en la tesis presentamos Teide, una propuesta que recibiendo varias reglas de enlazado las aplica de tal modo que mejora significativamente la precisión de las mismas sin reducir significativamente su cobertura. Por desgracia, Teide es computacionalmente costoso debido a que no aprende reglas. Debido a este motivo, presentamos Sorbas que aprende un tipo de reglas de enlazado que denominamos reglas de enlazado con contexto

A Model-Driven Architecture Approach to the Efficient Identification of Services on Service-oriented Enterprise Architecture

Service-Oriented Enterprise Architecture requires the efficient development of loosely-coupled and interoperable sets of services. Existing design approaches do not always take full advantage of the value and importance of the engineering invested in existing legacy systems. This paper proposes an approach to define the key services from such legacy systems effectively. The approach focuses on identifying these services based on a Model-Driven Architecture approach supported by guidelines over a wide range of possible service types

Towards a Rule Interchange Language for the Web

This articles discusses rule languages that are needed for a a full deployment of the SemanticWeb. First, it motivates the need for such languages. Then, it presents ten theses addressing (1) the rule and/or logic languages needed on the Web, (2) data and data processing, (3) semantics, and (4) engineering and rendering issues. Finally, it discusses two options that might be chosen in designing a Rule Interchange Format for the Web

IUPC: Identification and Unification of Process Constraints

Business Process Compliance (BPC) has gained significant momentum in research and practice during the last years. Although many approaches address BPC, they mostly assume the existence of some kind of unified base of process constraints and focus on their verification over the business processes. However, it remains unclear how such an inte- grated process constraint base can be built up, even though this con- stitutes the essential prerequisite for all further compliance checks. In addition, the heterogeneity of process constraints has been neglected so far. Without identification and separation of process constraints from domain rules as well as unification of process constraints, the success- ful IT support of BPC will not be possible. In this technical report we introduce a unified representation framework that enables the identifica- tion of process constraints from domain rules and their later unification within a process constraint base. Separating process constraints from domain rules can lead to significant reduction of compliance checking effort. Unification enables consistency checks and optimizations as well as maintenance and evolution of the constraint base on the other side.Comment: 13 pages, 4 figures, technical repor

Models simulation and interoperability using MDA and HLA

In the manufacturing context, there have been numerous efforts to use modeling and simulation tools and techniques to improve manufacturing efficiency over the last four decades. While an increasing number of manufacturing system decisions are being made based on the use of models, their use is still sporadic in many manufacturing environments. Our paper advocates for an approach combining MDA (model driven architecture) and HLA (High Level Architecture), the IEEE standard for modeling and simulation, in order to overcome the deficiencies of current simulation methods at the level of interoperability and reuse.Comment: 8 page