5989555
Enhanced Business Intelligence using EROCS
The EROCS technology automatically links unstructured data with relevant structured data from an external relational database. We demonstrate how EROCS can be used for enhancing business intelligence by allowing OLAP tools to analyze structured and unstructured data in a consolidated manner. Our demonstration showcases the use of EROCS in exploiting latent information in customer emails, which helps in building a complete view of the customer. This results in new insights about the business which are not possible with the existing state of the art.
22023969
Some issues in privacy data management
A method and apparatus is provided for aligning and press-fitting connector terminals into a substrate. The terminals are initially supported together by a bandolier strip, and an alignment tool aligns the terminals with apertures in the substrate to which the terminals will be press-fitted. A pressing tool engages the shanks of the terminals to fold the bandolier away from the terminals and thereafter press the terminals into the apertures.
7664269
Management of Conflicting Obligations in Self-Protecting Policy-Based Systems
Policy-based management of business systems is increasingly becoming the norm for autonomic computing since these systems can adapt to the changing needs and increasing complexity of the underlying organizations or enterprises. One of the vital characteristics of these systems is self-protection, ie., the ability to secure information and resources, by anticipating, detecting, identifying and protecting against any form of unauthorised access and permitting all authorised accesses based on the users' roles and pre-established policies. In this paper, we focus on one aspect of self-protecting autonomous systems, which is, how to automatically enforce privacy policies related to data handling, for compliance and auditing purposes. The automatic management of privacy sensitive information based on enterprise policies that are driven by a combination of user preferences, internal objectives and external regulations is a key aspect to any enterprise to prevent misuse of this information. These policies extend beyond simple authorization rules, and also mandate obligations to be enforced under certain conditions. One issue in the automatic enforcement of obligations is the presence of conflicts among different obligations which mandate different actions on the same resource, based on different conditions in which the resource is accessed. In this paper we propose algorithms for detecting and resolving conflicts among obligations in both static and runtime environments. We then briefly describe our prototype obligation management system with the conflict resolution module that achieves the automated enforcement of obligations for data-handling based on privacy policies
46325618
Policy Framework for Autonomic Data Managemen
3094106
LIPTUS: associating structured and unstructured information in a banking environment
Growing competition has made today's banks understand the value of knowing their customers better. In this paper, we describe a tool, LIPTUS, that associates the customer interactions (emails and transcribed phone calls) with customer and account profiles stored in an existing data warehouse. The associations discovered by LIPTUS enable analytics spanning the customer and account profiles on one hand and the meta-data associated or derived from the interaction (using text mining techniques) on the other. We illustrate the value derived from this consolidated analysis through specific customer intelligence applications. LIPTUS is today being extensively used in a large bank in India. A highlight of this paper is a discussion of the technical challenges encountered while building LIPTUS and deploying it on real-life customer data.
14792710
Enabling analysts in managed services for CRM analytics
Data analytics tools and frameworks abound, yet rapid deployment of analytics solutions that deliver actionable insights from business data remains a challenge. The primary reason is that on-field practitioners are required to be both technically proficient and knowledgeable about the business. The recent abundance of unstructured business data has thrown up new opportunities for analytics, but has also multiplied the deployment challenge, since interpretation of concepts derived from textual sources require a deep understanding of the business. In such a scenario, a managed service for analytics comes up as the best alternative. A managed analytics service is centered around a business analyst who acts as a liaison between the business and the technology. This calls for new tools that assist the analyst to be efficient in the tasks that she needs to execute. Also, the analytics needs to be repeatable, in that the delivered insights should not depend heavily on the expertise of specific analysts. These factors lead us to identify new areas that open up for KDD research in terms of 'time-to-insight' and repeatability for these analysts. We present our analytics framework in the form of a managed service offering for CRM analytics. We describe different analyst-centric tools using a case study from real-life engagements and demonstrate their effectiveness.
8269092
Color and texture fusion: application to aerial image segmentation and GIS updating
Abstract This paper describes a new algorithm for combining color and texture information for the segmentation of color images. The algorithm uses maximum likelihood classification combined with a certainty based fusion criterion. The algorithm was validated using mosaics of real color textures. It was also tested on real outdoor color scenes and aerial images. This algorithm is part of a more complex system, which is currently being designed to assist an operator in updating an old map of an area using aerial images.
6426062
Understanding Patterns of Technology Evolution: An Ecosystem Perspective
Understanding the dynamics of technology evolution — whether for the purposes of forecasting new product or technology infrastructure developments, or identifying the basis for future digital convergence in the global market— is a key challenge for innovators, senior managers, and policymakers. This research provides an overview of a new ecosystem model of technology evolution, the purpose of which is to structure these kinds of assessments and suggest reusable analysis structures to ensure that total environment of technological innovation is considered. We use examples from the end-user computing context and the electronics industry to identify five patterns of technology evolution that commonly occur. We also develop a state diagram-based approach to demonstrate the cyclical nature of technology evolution. Finally, we illustrate our findings using a case study on digital music technologies.
5555268
User Heterogeneity and its Impact on Electronic Auction Market Design: An Empirical Exploration1Research Essays
While traditional information systems research emphasizes understanding of end users from perspectives such as cognitive fit and technology acceptance, it fails to consider the economic dimensions of their interactions with a system. When viewed as economic agents who participate in electronic markets, it is easy to see that users' preferences, behaviors, personalities, and ultimately their economic welfare are intricately linked to the design of information systems. We use a data-driven, inductive approach to develop a taxonomy of bidding behavior in online auctions. Our analysis indicates significant heterogeneity exists in the user base of these representative electronic markets. Using online auction data from 1999 and 2000, we find a stable taxonomy of bidder behavior containing five types of bidding strategies. Bidders pursue different bidding strategies that, in aggregate, realize different winning likelihoods and consumer surplus. We find that technological evolution has an impact on bidders' strategies. We demonstrate how the taxonomy of bidder behavior can be used to enhance the design of some types of information systems. These enhancements include developing user-centric bidding agents, inferring bidders' underlying valuations to facilitate real-time auction calibration, and creating low-risk computational platforms for decision making.
15213964
Performance Indicators in a 802.11 WLAN Deployment
Organizations and Institutions across the world are increasingly deploying 802.11 based WLAN (in short Wi-Fi) to have mobility benefits in their operations. However, due to the nature of 802.11 technology, there are more number of challenges for an administrator in ensuring the optimum performance levels for the deployed WLAN compared to a Wired Network. Degraded performance leads to connectivity problems and low data throughput for wireless clients using the Wi-Fi network. In the presented work, we have identified a set of indicators whose values at any point of time signals a possible degradation in WLAN performance. Each of the identified indicators is explained and the behavior of same is illustrated using an example with respect to presence or absence of performance anomalies in a WLAN deployment.
43889456
Integrating User Preferences and Real-Time Workload in Information Services
We propose priority pricing as an on-line adaptive resource scheduling mechanism to manage real-time databases within organizations. These databases provide timely information for delay sensitive users. The proposed approach allows diverse users to optimize their own objectives while collectively maximizing organizational benefits. We rely on economic principles to derive priority prices by modeling the fixed-capacity real-time database environment as an economic system. Each priority is associated with a price and a delay, and the price is the premium (congestion toll resulting from negative externalities) for accessing the database. At optimality, the prices are equal to the aggregate delay cost imposed on all other users of the database. These priority prices are used to control admission and to schedule user jobs in the database system. The database monitors the arrival processes and the state of the system, and incrementally adjusts the prices to regulate the flow. Because our model ignores the operational intricacies of the real-time databases (e.g., intermediate queues at the CPU and disks, memory size, etc.) to maintain analytical tractability, we evaluate the performance of our pricing approach through simulation. We evaluate the database performance using both the traditional real-time database performance metrics (e.g., the number of jobs serviced on time, average tardiness) and the economic benefits (e.g., benefits to the organization). The simulation results, under various database workload parameters, show that our priority pricing mechanism not only maximizes organizational benefits but also outperforms in all aspects of traditional performance measures compared to frequently used database scheduling techniques, such as first-come-first-served, earliest deadline first and least slack first.