Driving the refactoring of Java Enterprise applications by evaluating the distance between application elements

Java Enterprise Applications (JEAs) are complex systems composed using various technologies that in turn rely on languages other than Java, such as XML or SQL. Given the complexity of these applications, the need to reverse engineer them in order to support further development becomes critical. In this paper we show how it is possible to split a system into layers and how is possible to interpret the distance between application elements in order to support the refactoring of JEAs. The purpose of this paper is to explore ways to provide suggestions about the refactoring operations to perform on the code by evaluating the distance between layers and elements belonging those layers. We split JEAs into layers by considering the kinds and the purposes of the elements composing the application. We measure distance between elements by using the notion of the shortest path in a graph. Also we present how to enrich the interpretation of the distance value with enterprise pattern detection in order to refine the suggestion about modifications to perform on the code.

Computer-aided diagnosis of carotid atherosclerosis based on ultrasound image statistics, laws' texture and neural networks

Quantitative characterisation of carotid atherosclerosis and classification into symptomatic or asymptomatic is crucial in planning optimal treatment of atheromatous plaque. The computer-aided diagnosis (CAD) system described in this paper can analyse ultrasound (US) images of carotid artery and classify them into symptomatic or asymptomatic based on their echogenicity characteristics. The CAD system consists of three modules: a) the feature extraction module, where first-order statistical (FOS) features and Laws' texture energy can be estimated, b) the dimensionality reduction module, where the number of features can be reduced using analysis of variance (ANOVA), and c) the classifier module consisting of a neural network (NN) trained by a novel hybrid method based on genetic algorithms (GAs) along with the back propagation algorithm. The hybrid method is able to select the most robust features, to adjust automatically the NN architecture and to optimise the classification performance. The performance is measured by the accuracy, sensitivity, specificity and the area under the receiver-operating characteristic (ROC) curve. The CAD design and development is based on images from 54 symptomatic and 54 asymptomatic plaques. This study demonstrates the ability of a CAD system based on US image analysis and a hybrid trained NN to identify atheromatous plaques at high risk of stroke.

Ragu: a free tool for the analysis of EEG and MEG event-related scalp field data using global randomization statistics

We present a program (Ragu; Randomization Graphical User interface) for statistical analyses of multichannel event-related EEG and MEG experiments. Based on measures of scalp field differences including all sensors, and using powerful, assumption-free randomization statistics, the program yields robust, physiologically meaningful conclusions based on the entire, untransformed, and unbiased set of measurements. Ragu accommodates up to two within-subject factors and one between-subject factor with multiple levels each. Significance is computed as function of time and can be controlled for type II errors with overall analyses. Results are displayed in an intuitive visual interface that allows further exploration of the findings. A sample analysis of an ERP experiment illustrates the different possibilities offered by Ragu. The aim of Ragu is to maximize statistical power while minimizing the need for a-priori choices of models and parameters (like inverse models or sensors of interest) that interact with and bias statistics.

Enterprise Integration of Smart Objects using Semantic Service Descriptions

Integrating physical objects (smart objects) and enterprise IT systems is still a labor intensive, mainly manual task done by domain experts. On one hand, enterprise IT backend systems are based on service oriented architectures (SOA) and driven by business rule engines or business process execution engines. Smart objects on the other hand are often programmed at very low levels. In this paper we describe an approach that makes the integration of smart objects with such backends systems easier. We introduce semantic endpoint descriptions based on Linked USDL. Furthermore, we show how different communication patterns can be integrated into these endpoint descriptions. The strength of our endpoint descriptions is that they can be used to automatically create REST or SOAP endpoints for enterprise systems, even if which they are not able to talk to the smart objects directly. We evaluate our proposed solution with CoAP, UDP and 6LoWPAN, as we anticipate the industry converge towards these standards. Nonetheless, our approach also allows easy integration with backend systems, even if no standardized protocol is used.

Critical Control Points for Profitability in the Cow-Calf Enterprise

Financial, economic, and biological data collected from cow-calf producers who participated in the Illinois and Iowa Standardized Performance Analysis (SPA) programs were used in this study. Data used were collected for the 1996 through 1999 calendar years, with each herd within year representing one observation. This resulted in a final database of 225 observations (117 from Iowa and 108 from Illinois) from commercial herds with a range in size from 20 to 373 cows. Two analyses were conducted, one utilizing financial cost of production data, the other economic cost of production data. Each observation was analyzed as the difference from the mean for that given year. The independent variable utilized in both the financial and economic models as an indicator of profit was return to unpaid labor and management per cow (RLM). Used as dependent variables were the five factors that make up total annual cow cost: feed cost, operating cost, depreciation cost, capital charge, and hired labor, all on an annual cost per cow basis. In the economic analysis, family labor was also included. Production factors evaluated as dependent variables in both models were calf weight, calf price, cull weight, cull price, weaning percentage, and calving distribution. Herd size and investment were also analyzed. All financial factors analyzed were significantly correlated to RLM (P < .10) except cull weight, and cull price. All economic factors analyzed were significantly correlated to RLM (P < .10) except calf weight, cull weight and cull price. Results of the financial prediction equation indicate that there are eight measurements capable of explaining over 82 percent of the farm-to-farm variation in RLM. Feed cost is the overriding factor driving RLM in both the financial and economic stepwise regression analyses. In both analyses over 50 percent of the herd-to-herd variation in RLM could be explained by feed cost. Financial feed cost is correlated (P < .001) to operating cost, depreciation cost, and investment. Economic feed cost is correlated (P < .001) with investment and operating cost, as well as capital charge. Operating cost, depreciation, and capital charge were all negatively correlated (P < .10) to herd size, and positively correlated (P < .01) to feed cost in both analyses. Operating costs were positively correlated with capital charge and investment (P < .01) in both analyses. In the financial regression model, depreciation cost was the second critical factor explaining almost 9 percent of the herd-to-herd variation in RLM followed by operating cost (5 percent). Calf weight had a greater impact than calf price on RLM in both the financial and economic regression models. Calf weight was the fourth indicator of RLM in the financial model and was similar in magnitude to operating cost. Investment was not a significant variable in either regression model; however, it was highly correlated to a number of the significant cost variables including feed cost, depreciation cost, and operating cost (P < .001, financial; P < .10, economic). Cost factors were far more influential in driving RLM than production, reproduction, or producer controlled marketing factors. Of these cost factors, feed cost had by far the largest impact. As producers focus attention on factors that affect the profitability of the operation, feed cost is the most critical control point because it was responsible for over 50 percent of the herd-to-herd variation in profit.

The Interplay of Power and Trust in Platform Ecosystems of the Enterprise Application Software Industry

In recent years, the formerly oligopolistic Enterprise Application Software (EAS) industry began to disintegrate into focal inter-firm networks with one huge, powerful, and multi-national plat-form vendor as the center, surrounded by hundreds or even thousands of small, niche players that act as complementors. From a theoretical point of view, these platform ecosystems may be governed by two organizing principles - trust and power. However, it is neither from a practical nor from a theoretical perspective clear, how trust and power relate to each other, i.e. whether they act as complements or substitutes. This study tries to elaborate our understanding of the relationship of trust and power by exploring their interplay using multi-dimensional conceptual-izations of trust and power, and by investigating potential dynamics in this interplay over the course of a partnership. Based on an exploratory multiple-case study of seven dyadic partner-ships between four platform vendors, and seven complementors, we find six different patterns of how trust and power interact over time. These patterns bear important implications for the suc-cessful management of partnerships between platform vendors and complementors, and clarify the theoretical debate surrounding the relationship of trust and power.

Integration of WSNs into enterprise systems based on semantic physical business entities

Internet of Things based systems are anticipated to gain widespread use in industrial applications. Standardization efforts, like 6L0WPAN and the Constrained Application Protocol (CoAP) have made the integration of wireless sensor nodes possible using Internet technology and web-like access to data (RESTful service access). While there are still some open issues, the interoperability problem in the lower layers can now be considered solved from an enterprise software vendors' point of view. One possible next step towards integration of real-world objects into enterprise systems and solving the corresponding interoperability problems at higher levels is to use semantic web technologies. We introduce an abstraction of real-world objects, called Semantic Physical Business Entities (SPBE), using Linked Data principles. We show that this abstraction nicely fits into enterprise systems, as SPBEs allow a business object centric view on real-world objects, instead of a pure device centric view. The interdependencies between how currently services in an enterprise system are used and how this can be done in a semantic real-world aware enterprise system are outlined, arguing for the need of semantic services and semantic knowledge repositories. We introduce a lightweight query language, which we use to perform a quantitative analysis of our approach to demonstrate its feasibility.

Linked services for M2M communication with Enterprise IT systems

Linking the physical world to the Internet, also known as the Internet of Things, has increased available information and services in everyday life and in the Enterprise world. In Enterprise IT an increasing number of communication is done between IT backend systems and small IoT devices, for example sensor networks or RFID readers. This introduces some challenges in terms of complexity and integration. We are working on the integration of IoT devices into Enterprise IT by leveraging SOA techniques and Semantic Web technologies. We present a SOA based integration platform for connecting WSNs and large enterprise business processes. For ensuring interoperability our platform is based on Linked Services. These are thoroughly described, machine-readable, machine-reasonable service descriptions.

Linked Services for Enabling Interoperability in the Sensing Enterprise

In future, the so called “sensing enterprise”, as part of the Future Internet, will play a crucial role in the success or the failure of an enterprise. We present our vision of an enterprise interacting with the physical world based on a retail scenario. One of the main challenges is the interoperability not only between the enterprise IT systems themselves, but also between these systems and the sensing devices. We will argue that semantically enriched service descriptions, the so called linked services will ease interoperability between two or more enterprises IT systems, and between enterprise systems and the physical environment.