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.
10962942
Towards bringing database management task in the realm of IT non-experts
Internet enabled services have led to an increase in the size and complexity of the database making the database administration task very complex. This necessitates the hiring of skilled personnel for the management of data, which is a bane for industries, especially in developing countries. We at IBM India research Lab, have been developing technologies (i.e. database administration tools), wherein the database administrator, who may be inexperienced person, can define error-free policies for managing and maintaining the distributed information repositories, and can define data access privileges to users at fine as well as at coarse-grained level using natural language like constructs. These policies represent the actions that need to be carried out when specific database/temporal events occur within the system or externally and are notified to the system. In this paper, we discuss the architecture of our policy based database administration and access control system and outline some of the technical challenges in this area.
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
20290151
Web Page Ranking Based on Events
Search results of web search engines are displayed according to their ranking function, which is a function of the in-links and the out-links of the web page. Users are generally overwhelmed by the thousands of results retrieved by the search engine, few of which are useful. Most of the search engine users are interested in the latest information about the searched keywords. Such pages containing the latest information about an event (or incident) are not always ranked high by the search engines due to the lack of sufficient in-links pointing to these web pages. For example, if a search query “DaWaK” is given to Google, the users are generally interested in the latest information i.e., the home page of DaWaK 2004 conference, which is not ranked in the top 10 results returned by Google. In this paper we address the problem of ranking the web pages based on events (or incidents) related to the searched keywords. We provide a method for finding patterns that constitute events in the search results returned by a conventional search engine and then rank the web pages based on these event patterns. We also describe a mechanism whereby our technique can be used inside the ranking function of a conventional search engine such as Google. We provide experimental results that validate the efficiency and use of our technique in capturing the user intentions.
116973756
Surface and volumetric segmentation of complex 3-D objects using parametric shape models
The problem of part definition, description, and decomposition is central to the shape recognition systems. In this dissertation, we develop an integrated framework for segmenting dense range data of complex 3-D scenes into their constituent parts in terms of surface and volumetric primitives. Unlike previous approaches, we use geometric properties derived from surface, as well as volumetric models, to recover structured descriptions of complex objects without a priori domain knowledge or stored models. 
To recover shape descriptions, we use bi-quadric models for surface representation and superquadric models for object-centered volumetric representation. The surface segmentation uses a novel approach of searching for the best piecewise description of the image in terms of bi-quadric (z = f(x, y)) models. It is used to generate the region adjacency graphs, to localize surface discontinuities, and to derive global shape properties of the surfaces. A superquadric model is recovered for the entire data set and residuals are computed to evaluate the fit. The goodness-of-fit value based on the inside-outside function, and the mean-squared distance of data from the model provide quantitative evaluation of the model. The qualitative evaluation criteria check the local consistency of the model in the form of residual maps of overestimated and underestimated data regions. 
The control structure invokes the models in a systematic manner, evaluates the intermediate descriptions, and integrates them to achieve final segmentation. Superquadric and bi-quadric models are recovered in parallel to incorporate the best of the coarse-to-fine and fine-to-coarse segmentation strategies. The model evaluation criteria determine the dimensionality of the scene, and decide whether to terminate the procedure, or selectively refine the segmentation by following a global-to-local part segmentation approach. The control module generates hypotheses about superquadric models at clusters of underestimated data and performs controlled extrapolation of the part-model by shrinking the global model. As the global model shrinks and the local models grow, they are evaluated and tested for termination or further segmentation. 
We present results on real range images of scenes of varying complexity, including objects with occluding parts, and scenes where surface segmentation is not sufficient to guide the volumetric segmentation. We analyze the issue of segmentation of complex scenes thoroughly by studying the effect of missing data on volumetric model recovery, generating object-centered descriptions, and presenting a complete set of criteria for the evaluation of the superquadric models. We conclude by discussing the applications of our approach in data reduction, 3-D object recognition, geometric modeling, automatic model generation, object manipulation, and active vision.
8704912
Replicating Online Yankee Auctions to Analyze Auctioneers' and Bidders' Strategies
We present a simulation approach that provides a relatively risk-free and cost-effective environment to examine the decision space for both bid takers and bid makers in web-based dynamic price setting processes. The applicability of the simulation platform is demonstrated for Yankee auctions in particular. We focus on the optimization of bid takers' revenue, as well as on examining the welfare implications of a range of consumer-bidding strategies--some observed, some hypothetical. While these progressive open discriminatory multiunit auctions with discrete bid increments are made feasible by Internet technologies, little is known about their structural characteristics, or their allocative efficiency. The multiunit and discrete nature of these mechanisms renders the traditional analytic framework of gametheory intractable (Nautz and Wolfstetter 1997). The simulation is based on theoretical revenue generating properties of these auctions. We use empirical data from real online auctions to instantiate the simulation's parameters. For example, the bidding strategies of the bidders are specified based on three broad bidding strategies observed in real online auctions. The validity of the simulation model is established and subsequently the simulation model is configured to change the values of key control factors, such as the bid increment. Our analysis indicates that the auctioneers are, most of the time, far away from the optimal choice of bid increment, resulting in substantial losses in a market with already tight margins. The simulation tool provides a test bed forjointly exploring the combinatorial space of design choices made by the auctioneer's and the bidding strategies adopted by the bidders. For instance, a multinomial logit model reveals that endogenous factors, such as the bid increment and the absolute magnitude of the auction have a statistically significant impact on consumer-bidding strategies. This endogeniety is subsequently modeled into the simulation to investigate whether the effects are significant enough to alter the optimal bid increments or auctioneer revenues. Additionally, we investigate hybrid-bidding strategies, derived as a combination of three broad strategies, such as jump bidding and strategic-at-margin (SAM) bidding. We find that hybrid strategies have the potential of significantly altering bidders' likelihood of winning, as well as their surplus.
2100365
Simulating online Yankee auctions to optimize sellers revenue
The online Yankee auction sells multiple units of the same good to multiple buyers using an ascending and open auction mechanism. One of the important controllable factors of the Yankee auction is the minimum bid increment specified at the beginning of an auction. Bidders can bid only in the multiples of this bid increment even if they are willing to bid higher than the minimum required bid. This paper presents a simulation approach to optimize sellers' revenue, which is based on theoretical properties of Yankee auctions and utilizes data from real auctions to replicate a given auction. The simulation model can be configured to change the value of bid increment and explore whether a given auction used the optimal bid increment. Our analysis indicates that the auctioneers are, most of the time, far away from optimal choice of bid increment, resulting in substantial losses in a market with already tight margins.
17420676
A workstation's communication performance benchmark
In this paper we propose a communication benchmark suite: BTU(Bits To the User). In the BTU benchmarking process, the vendor supplies a workstation running a UNIX* operating system, we install our benchmark program and connect the workstation to a testbed which emulates a LAN/WAN environment. The benchmark run will submit the workstation to a carefully designed combination of tests. The result is a predictor of what the user, at the application level, can expect in terms of bits sent to or received from a remote host. Our benchmark takes into account concurrent activities such as CPU and I/O activities which compete for resources on the test machine. The combination of these activities and concurrent activities on the network will interfere with the test machine’s communication performance. The automated BTU benchmark test suite produces results at various levels of abstractions ranging from a single number, characterizing average performance in the style of SPEC92, to a TCP time sequence chart for abnormal behaviors. We intend to serve the user of a workstation by providing information which should enable the users to make a reasonable judgment when acquiring a workstation with a specific configuration within certain cost constraints. Our results when used along with existing benchmarks will make a powerful combination for predicting overall performance. Neither price nor SPECmarks are as good a predictor of communication performance as that measured by BTUs. We conclude this paper with a discussion of extensions to faster networks and our plans for having the community accept this proposal as a communication benchmark.
39076736
Designing Intelligent Software Agents for Auctions with Limited Information Feedback
This paper presents analytical, computational, and empirical analyses of strategies for intelligent bid formulations in online auctions. We present results related to a weighted-average ascending price auction mechanism that is designed to provide opaque feedback information to bidders and presents a challenge in formulating appropriate bids. Using limited information provided by the mechanism, we design strategies for software agents to make bids intelligently. In particular, we derive analytical results for the important characteristics of the auction, which allow estimation of the key parameters; we then use these theoretical results to design several bidding strategies. We demonstrate the validity of designed strategies using a discrete event simulation model that resembles the mechanisms used in treasury bills auctions, business-to-consumer (B2C) auctions, and auctions for environmental emission allowances. In addition, using the data generated by the simulation model, we show that intelligent strategies can provide a high probability of winning an auction without significant loss in surplus.