Sumali Conlon

Distributed manufacturing support systems: the integration of distributed group support systems with manufacturing support systems

Current Manufacturing Support Systems (MSS), such as Computer-aided Manufacturing (CAM) Systems, Computer-aided Design (CAD) Systems, Computer Integrated Manufacturing (CIM) Systems, Material Resource Planning (MRP) Systems, and Manufacturing Accounting Control (MAC) Systems, are mostly independent systems that are operated in limited decision spaces, provide mainly formal and quantitative information, and thus pursue a goal of local optimization. To assist modern manufacturing in meeting the needs for integration, communication, collaboration, and decision making, we introduce the concept of integrating MSS with Distributed Group Support System (DGSS) into a Distributed Manufacturing Support System (DMSS). A rigorous system design approach is taken to model the manufacturing information requirements from a global perspective and pattern decision making processes within the structural (organizational design) and infrastructural (information system design) elements of manufacturing. The result is a conceptual DMSS design that provides an intelligent interface, accommodates incremental manufacturing integration, offers controllable message exchange facilities, and allows configurable communication networks.

The economics of natural language interfaces: natural language processing technology as a scarce resource

This paper discusses appropriate application areas for natural language interfaces (NLIs) to databases. This requires comparing NLIs with competing approaches, including other user-friendly interfaces, and training of users with less user-friendly interfaces. Also, since NLI technology is still limited, users may need to learn how to use NLIs themselves. This suggests that NLI popularity may snowball at some point, as users become familiar with NLIs. We use a simple prototype NLI to illustrate when NLIs can achieve flexibility unattainable by simpler interfaces. Currently existing commercial NLIs and application-specific customization are also discussed.

Information Extraction Agents for Service-Oriented Architecture Using Web Service Systems: A Framework

In some business domains, such as financial investment services, maintaining current information is a serious challenge, as markets evolve by the hour. This creates a demand for continually-updated information systems to support business decision-makers. Many sources of domain information are online documents containing unstructured text. For the information encoded in these natural language texts to be usable by systems, it must be extracted and reshaped into forms which those systems can recognize. The goal of this research is to investigate ways to exploit information-rich, online source texts in order to automatically update information in web service (WS) systems implemented in service-oriented architecture (SOA) environments. We propose a general framework for service-oriented web service systems that incorporates information extraction (IE) components capable of handling unstructured text. Since IE systems are sophisticated and difficult to implement, performance and economic considerations suggest that firms with special expertise should perform these tasks and sell the services to service customers. Our prototype information extraction system, which can be embedded in a WS system, is described to illustrate the framework.

A Rule-Based System to Extract Financial Information

Extracting up-to-date information from financial documents can be important in making investment decisions. However, the unstructured nature and enormity of the volume of such data makes manual analysis tedious and time consuming. Information extraction technology can be applied to automatically extract the most relevant and precise financial information. This paper introduces a rule-based information extraction methodology for the extraction of highly accurate financial information to aid investment decisions. The methodology includes a rule-based symbolic learning model trained by the Greedy Search algorithm and a similar model trained by the Tabu Search algorithm. The methodology has been found very effective in extracting financial information from NASDAQ-listed companies. Also, the Tabu Search based model performed better than some well-known systems. The simple rule structure makes the system portable and it should make parallel processing implementations easier.

Terrorism Information Extraction from Online Reports

Many documents containing information about intelligence and security issues are available both in printed and electronic formats. In this research, we built an experimental system to extract intelligence and security information from electronic documents. Our system, CAINES, is based on a knowledge engineering approach and relies on sublanguage analysis techniques. CAINES performs syntactic and semantic analysis and uses lexicons of various categories of terms. The system is able to extract certain types of information from reports on terrorist incidents posted by the National Counterterrorism Center (NCTC), such as what happened and the results of the incidents.

Optimal use of an information retrieval system

This article examines a simple model of an information retrieval (IR) technology, and uses this model to examine the optimal use of such a technology. In particular, we show that, if we understand optimal behavior, then we can (a) use observed behavior to shed light on the users objective function, and (b) analyze how a users behavior changes as her environment changes, for various specifications of the users objective function. We also show how user satisfaction varies with the underlying stochastic behavior of the retrieval process.

A framework to explore innovation at SAP through bibliometric analysis of patent applications

We provide an analysis of innovation at SAP using bibliometric analysis of patents.A rotation and sort procedure was applied to the text.A frequent itemset analysis was performed using the word occurrence matrix.A blockmodeling algorithm was applied to explore relatedness between terms.We integrate text and network analysis tools to perform bibliometric analysis. Easily accessible patent databases and advances in technology have enabled the exploration of organizational innovation through the analysis of patent records. However, the textual content of patents presents obstacles to gleaning useful information. In this study, we develop an expert system framework that utilizes text and data mining procedures for analyzing innovation through textual patent data. Specifically, we use patent titles representing the innovation activity at one company (SAP) and perform a bibliometric analysis using our proposed framework. Enterprise software, of which SAP is a pioneering developer, must serve a wide assortment of functions for companies in many different industries. In addition, SAPs sole focus is on enterprise software and it is a market leader in the category with substantial patent activity over the last decade. Using our framework to analyze SAPs patent activity provides a demonstration of how our bibliometric analysis can summarize and identify trends in innovation in a large software company. Our results illustrate that SAP has a breadth of innovative activity spread over the three-tier software engineering architecture and a lack of topical repetition indicative of limited depth. SAPs innovation is also seen to emphasize data management and quickly integrate emerging technologies. Results of an analysis on any company following our framework could be used for a variety of purposes, including: to examine the scope and scale of innovation of an organization, to examine the influence of technological trends on businesses, or to gain insight into corporate strategy that could be used to aid planning, investment, and purchasing decisions.

Maximizing the Value of Student Ratings Through Data Mining

Student ratings of instruction are an important means of assessment within universities and have been the focus of much study over the last 50 years. Until very recently it has been difficult to perform meaningful analysis of student narrative comments given that most universities collected them as hand-written notes. This work uses statistical and text mining techniques to analyze a data set consisting of over 1 million student comments that were collected using an online process. The methodology makes use of positive and negative “category vectors” representing instructor characteristics and a domain-specific lexicon. Sentiment analysis is used to detect and gauge attitudes embedded in comments about each category. The methodology is validated using three approaches, two quantitative and one qualitative. While useful to individual instructors and administrators, it is only through data mining that student perceptions of teaching can be analyzed en masse to inform and influence the educational process.

Text Analysis of Green Supply Chain Practices in Healthcare

ABSTRACT The growing demand for environmentally safe products in the healthcare industry requires the United States to implement environmentally sustainable principles in healthcare systems. In this research, we build an experimental text analysis system which can analyze large collections of documents related to healthcare systems, in order to extract information about their green supply chain practices. Collocation and sequential pattern mining analysis techniques are applied in the experimental system. When tested and evaluated, the system is found to have a recall of 72% and a precision of 73.1%.

Extraction of financial information from online business reports

CAINES, Content Analysis and INformation Extraction System, employs a semantic based information extraction (IE) methodology through a design science approach to extract unstructured text from the Web. Our system was knowledge-engineered and tested on an active business database by experts who use the database regularly to perform their job functions. We believe that by heavily involving business experts, we are able to advance our thinking about IS research. CAINES extracts information to meet three objectives that were deemed important by our experts: (1) understand what current market conditions impacted the growth of certain balance sheets (2) summarize managements discussion of potential risks and uncertainties (3) identify significant financial activities including mergers, acquisitions, and new business segments. These objectives were developed based on the advice of financial experts who regularly analyze financial reports. A total of 21 online business reports from the EDGAR database, each averaging about 100 pages long, were used in this study. Based on financial expert opinions, extraction rules were created to extract information from financial reports. Using CAINES, one can extract information about global and domestic market conditions, market condition impacts, and information about the business outlook. User testing of CAINES resulted in recall of 85.91%, precision of 87.16%, and an F-measure of 86.46%. Speed with CAINES was also greater than manually extracting information. Users agreed that CAINES quickly and easily extracts unstructured information from financial reports on the EDGAR database. This study highlights the significance of creating a semantic based IE system that addresses practical business issues and solves a true business problem with the knowledge of business experts.