Abstract: The business process modeling presents a global vision of an organization. This characteristic allows for a better understanding of: the dynamics of the company, and the relationships that are given inside it and with its environment. Therefore, it is the best technique to align the development with the organization's goals. In this context, the role of modeling languages in the Business Processes specification is fundamental. Choosing the most appropriate language for specifying the company processes is an important and critical decision. This is because the models help to improve its performance and evolution avoiding many risk factors. In this paper, an evaluation strategy of business process modeling languages based in LSP is proposed. The main goal is to provide companies with a framework for the selection of the best business process modeling languages.

Abstract: The goal of this keynote paper is to argue for a unifying theoretical foundation for software engineering. I believe that one of the reasons for our lack of rigor compared to physical and behavioral sciences is that we have not given enough attention to the theories that underpin our work, both as software engineers and as software engineering researchers. I present my general theory about software engineering and then propose two simple theories, D and E as the basis for laying out a unified theoretical foundation for software engineering and software engineering research. Software Engineering consists of two logical parts: design and empirical evaluation (both terms used in their broadest senses). I propose theory D to as the theoretical basis for the design part, and theory E as the theoretical basis for empirical evaluation. These two theories are then composed in various ways to lay out a space (a taxonomy, or ontology if you will) for software engineering. Finally, I claim that software engineering and software engineering research (both fully integrated with empirical evaluations) are models for the atomic and composed theories.

Abstract: Data warehouses (DWs) and OLAP systems are essential tools in organizational decision making. However, users of these systems are often provided with complex data models and system-specific GUIs and dashboards to analyze the data and generate reports. Available DW query languages like MDX and Oracle OLAP support only numeric data and moreover require skilled DW developers to design queries. This can strain the analyst and restrict effective data analysis. In this paper, we provide a solution to this problem by introducing a powerful textual query language based entirely on the abstract datacube metaphor for multidimensional analysis. This language called the Cube Analysis Language (CAL) captures the full dimensionality of the data while providing an extensible query interface for the analyst. CAL includes three basic components: the Cube Definition Language (CDL), to design and develop the cube, the Cube Manipulation Language (CML), to manipulate and alter the cube, and the Cube Query and Analysis Language (CQAL), to navigate through cubes and aggregate data in the DW for analysis. CDL, CML and CQAL are designed to be explicit user-level concepts that work easily with existing OLAP languages and logical DW designs. To demonstrate its functionality we have implemented the CAL query processor for Mondrian (an open-source OLAP tool) and tested it using an example business sales dataset on Postgres.

Abstract: In this paper we are concerned in looking at different ways for calculating the strength of Association Rules in Market Basket data. The significance of Association rules is measured via two measures support and confidence and the way these measures are used to determine strong rules. In the realm of Market Basket Research these measures can be used to find the strength of the rules in a particular transaction of the form, "When a customer buys items A&B also buys item C". The first portion of this paper illustrates the usage of the method of Maximum Likelihood for Point Estimation and gives an idea how the maximum likelihood estimator can also be used for predicting the confidence of an association rule. The second portion of the paper mainly describes with examples how maximum likelihood function can be used for calculating the collective confidence of association rules.

Abstract: XML databases have been widely used for web applications to facility data exchanges through internet. Traditional database systems, including XML systems, usually handle precise and well-defined data. In the real world, there exist data that is uncertain and ambiguous. Fuzzy logic reflects human nature to express and evaluate the world in a vague manner. This paper describes our approach of incorporating fuzzy logic into XML database systems. Our system is built on top of an existing XML database management system, which allows the definition and storage of fuzzy data in addition to crisp information. We defined a query language based on the XQuery standard that allows users to query the underlying database using fuzzy expressions. Another important issue in a database system is integrity constraint management. Active rules, also named Event-Condition-Active rules, have mainly been used in relational database systems for integrity control, which are promising features for XML databases. Our fuzzy system includes an active rule-based subsystem that supports the specification and execution of active rules. Users can use fuzzy expressions in the rule definition to declaratively define business logic. The system supports different types of events, including temporal events and composite events, in addition to traditional mutation events.

Abstract: In the past several years, student retention has become a major topic of interest in academic circles. The discussion has become even more intricate due to economic and legislative pressures, and administrative directives. As a result, many universities have started to look toward solutions for identifying and reaching out to at-risk students. In this paper, we will discuss a homegrown early-warning and retention system known as IU-RETAIN which was initiated at Indiana University South Bend in 2007. Contrary to many university IT systems that are developed and operated by the university IT services, the IU-RETAIN system was designed and built by and for faculty and has been in use since 2007. The system has been quite successful in serving approximately 8,700 students per semester. Voluntary faculty participation was at 81% during the spring semester of 2011. More importantly, overall campus retention numbers have significantly improved.

Abstract: This paper reports on experience with the engineering and empirical evaluation of data management software that stores objects in collections like the ArrayList or Vector. While many programs may retrieve an object from a collection by iteratively evaluating each object according to a set of condition(s), this imperative retrieval process becomes more challenging and error-prone as it applies many complex criteria to find the matching objects in multiple collections. Query languages for unstructured Java virtual machine (JVM) heaps present an alternative to the imperative approach for finding the matching objects. Using a benchmarking framework that measures the performance of declarative approaches to identifying certain objects in the JVM heap, this paper empirically evaluates two query languages, JQL and JoSQL. Both the experiences and the experimental results reveal trade-offs in the performance and overall viability of the query languages and the imperative approaches.