In this paper, we propose a new technique for managing multiple type objects in systems such as GIS and CAD. The novel concept of our mechanism is to separate the objects in a data structure according to the type identifier of the objects. The type identifier is a binary number that signifies a bit pattern corresponding to the type of the object. By means of the type identifier, a data structure can manage not only the space information of objects but also the types of objects. Therefore, in our mechanism, access performance does not depend on the number of all objects, as in most of the past methods, but rather on the number of objects in the type identifier. Also, access and memory performance do not deteriorate even if the types of objects are not independent. The performance of our system is evaluated by simulation tests.
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.
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.
Cloud computing is changing the way we work and the way we live. Cloud providers host their Clouds at their data centers. However, the data centers might be far away from end users. The end users would experience long response latency. Client-side proxy servers are close to the end users. They are widely deployed around the Internet. They are the ideal platforms to cache/replicate Cloud applications. However, current proxy servers do not support caching and execution of Cloud applications. In this paper, we utilize virtualization technology to expand the functionalities of proxy servers to support caching and execution of Cloud applications. Specifically, we propose a system model that consists of a virtualization server, a Cloud access request interception mechanism, proxy server extension modules, and a migration protocol. Heterogeneous services hosted by different Cloud providers can be migrated from the Clouds to the virtualization server at the proxy server side. Also, a migration cost function is designed to assist service migration decision making. With our approach, Cloud applications can be moved close to the end users. Our approach can reduce the user perceived response latency and thus it can achieve better user experience during Cloud access. We have utilized the industrial standard e-commerce benchmark TPC-W to conduct experimental studies. The experimental results show that our approach significantly reduces client response latency.
Due to the dynamism of business, it is very important to support continuous improvement and automation of business processes. The Workflow Management Systems WMS is one of the most significant technologies for such automation. Likewise, the quality of business process models is essential to facilitate understandability and adaptability of models to the constant changes experienced by the processes they represent. Therefore, it is fundamental to have tools to choose from several modeling alternatives, the most appropriate to the business needs. It is also essential to have tools to measure the quality of these models. Therefore, we propose a set of metrics for measuring the quality of workflow models based on their adaptability and understandability. In addition, we introduce the results of the first practical validation of the metrics through a comparative experiment of two Workflow modeling languages: YAWL Vs UML Activity Diagrams 2.0.
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.
A steel thread is a software engineering construct that identifies the most important execution paths, including software and hardware elements, through a computer system, while meeting business objectives and demonstrating executable architecture. Steel threads are often used in the context of defining software system architecture. Although there have been references to steel threads in software engineering literature, it is hard to find clear definitions and usage of steel threads in the software industry or among the research community. This paper provides an overview of steel threads in software architecture design and development as well as presenting the contexts of steel threads. In addition, we show how to identify important scenarios and execution paths to construct steel threads and discuss the contexts under which steel threads are applicable in the software development life cycle. We also discuss the roles of steel threads in system development as well as their usability and applicability. Finally, a case study of the use of steel threads in a software system is presented.
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.
Since the advent of digital image technology and remote sensing imagery (RSI), massive amount of image data has been collected worldwide. For example, since 1972, NASA and U.S. Geological Survey through the Landsat Data Continuity Mission, has been capturing images of Earth down to 15 meters resolution. Since image clustering is time-consuming, much of this data is archived even before analysis. In this paper, we propose a novel and extremely fast algorithm called FAUST P or Fast Attribute-based Unsupervised and Supervised Table Clustering for images. Our algorithm is based on Predicate-Trees which are compressed, lossless and data-mining-ready data structures. Without compromising much on the accuracy, our algorithm is fast and can be effectively used in high-speed image data analysis.
