Rema Padman

Incremental hierarchical clustering of text documents

Incremental hierarchical text document clustering algorithms are important in organizing documents generated from streaming on-line sources, such as, Newswire and Blogs. However, this is a relatively unexplored area in the text document clustering literature. Popular incremental hierarchical clustering algorithms, namely Cobweb and Classit, have not been widely used with text document data. We discuss why, in the current form, these algorithms are not suitable for text clustering and propose an alternative formulation that includes changes to the underlying distributional assumption of the algorithm in order to conform with the data. Both the original Classit algorithm and our proposed algorithm are evaluated using Reuters newswire articles and Ohsumed dataset.

Assessing data quality in accounting information systems

Accounting information systems maintain and produce the data used by organizations to plan, evaluate, and diagnose the dynamics of operations and financial circumstances [1]. In addition to intraorganizational usage, the data produced by these systems is reported to external stakeholders such as stockholders or government agencies. In light of these external reporting requirements, AIS have been subject to considerable scrutiny and the data produced by these systems—financial statements are good examples—is required to be certifiably free of specific types of errors. These data quality assessments of AIS are conducted by professional assessors known as auditors [2, 12]. David Kaplan, Ramayya Krishnan, Rema Padman, and James Peters

Project Contracts and Payment Schedules: The Client's Problem

Contractual agreements have assumed significant complexity in recent times because of the emergence of strategies like outsourcing and partnering in the successful completion of large software development, manufacturing, and construction projects. A client and contractor enter into an agreement for a project either by bidding or negotiation. Effective and efficient bidding, negotiation, and subsequent monitoring are hindered by the lack of appropriate decision support tools for the management of project finances. Progress payments to the contractor are an important issue in project management because of their potential impact on project finances and activity schedules. In this paper, we consider the problem of simultaneously determining the amount, location, and timing of progress payments in projects from a clients perspective. We develop three mixed-integer linear programming models, based on some practical methods of determining payment schedules from different types of project contracts. We discuss properties of the models and draw insights about the characteristics of optimal payment schedules obtained with each model by an experimental study on a sample of 10 small projects. Our analysis shows that, contrary to current practice, the client obtains the greatest benefit by scheduling the project for early completion such that the payments are not made at regular intervals. It is also cost effective for the client to make payments either in the early stages of the project or toward the end, even though this causes considerable variation in the time gap between payments. We also evaluate the impact of the clients preferred payment schedules on the contractors finances and activity schedules, and draw some conclusions on the interdependence of payment and project parameters on the objectives of both parties entering the contractual agreement.

Quantity discounts and quality premia for illicit drugs

Abstract This article explores quantity discounts and quality (purity) premia in the prices of illicit drugs. It examines several models of how drug prices might depend on transaction size. A simple relation implied by a tree model of the domestic distribution network fits data provided by the Western States Information Network for 1984–1991 quite well for various illicit drugs. Quality premia are less well explained. It is observed that price is not a function of pure quantity alone; customers pay more for 2 grams at a given purity than they do for 1 gram at double that purity. Nevertheless, some purity premia are observed for white heroin, brown heroin, and powder cocaine, although not for methamphetamines, crack, or heroin tar. The estimated coefficients reflect known phenomena such as the collapses in the prices of cocaine and black tar heroin; intuitively reasonable but undocumented phenomena, such as discounts for brown heroin near the Mexican border; and some unexpected results, such as an apparent...

On Data Reliability Assessment in Accounting Information Systems

The need to ensure reliability of data in information systems has long been recognized. However, recent accounting scandals and the subsequent requirements enacted in the Sarbanes-Oxley Act have made data reliability assessment of critical importance to organizations, particularly for accounting data. Using the accounting functions of management information systems as a context, this paper develops an interdisciplinary approach to data reliability assessment. Our work builds on the literature in accounting and auditing, where reliability assessment has been a topic of study for a number of years. While formal probabilistic approaches have been developed in this literature, they are rarely used in practice. The research reported in this paper attempts to strike a balance between the informal, heuristic-based approaches used by auditors and formal, probabilistic reliability assessment methods. We develop a formal, process-oriented ontology of an accounting information system that defines its components and semantic constraints. We use the ontology to specify data reliability assessment requirements and develop mathematical-model-based decision support methods to implement these requirements. We provide preliminary empirical evidence that the use of our approach improves the efficiency and effectiveness of reliability assessments. Finally, given the recent trend toward specifying information systems using executable business process models (e.g., business process execution language), we discuss opportunities for integrating our process-oriented data reliability assessment approach-developed in the accounting context-in other IS application contexts.

Heuristic scheduling of capital constrained projects

Abstract The movement to product and process development projects that involve joint ventures among strategic partners, as well as the increasing prevalence of projects within organizations has led to increased implementation of project scheduling methods. It is frequently the case that a capital constraint is placed on a project, thus limiting the number and value of activities that can be scheduled to occur simultaneously. However, the quantity of capital available to schedule activities can increase as additional cash is received as progress payments for completed activities. Since the project managerso objective is to maximize project Net Present Value (NPV), it is important for the manager to develop a schedule that balances the early receipt of progress payments (which improve NPV and increase the capital balance available), with the delay of particularly large expenditures. Due to the intractability of optimal methods, the use of heuristic methods is required to solve problems of practical size. This paper presents the first test of heuristic methods for solving this problem. We use information from a relaxed optimization-guided model that employs information from the unconstrained NPV-optimal problem in heuristic procedure for solving the capital constrained problem. An experimental design is employed to test the heuristics that includes multiple factor levels for a number of project characteristics, including capital utilization, frequency of progress payments, and project network structure. The results indicate very good relative performance for the optimization-guided procedures as compared to two benchmark heuristics.

Heuristic scheduling of resource-constrained projects with cash flows

Resource-constrained project scheduling with cash flows occurs in many settings, ranging from research and development to commercial and residential construction. Although efforts have been made to develop efficient optimal procedures to maximize the net present value of cash flows for resource-constrained projects, the inherent intractability of the problem has led to the development of a variety of heuristic methods to aid in the development of near-optimal schedules for large projects. This research focuses on the use of insights gained from the solution of a relaxed optimization model in developing heuristic procedures to schedule projects with multiple constrained resources. It is shown that a heuristic procedure with embedded priority rules that uses information from the revised solution of a relaxed optimization model increases project net present value. The heuristic procedure and nine different embedded priority rules are tested in a variety of project environments that account for different network structures, levels of resource constrainedness, and cash-flow parameters. Extensive testing with problems ranging in size from 21 to 1000 activities shows that the new heuristic procedures dominate heuristics using information from the critical path method (CPM), and in most cases outperform heuristics from previous research. The best performing heuristic rules classify activities into priority and secondary queues according to whether they lead to immediate progress payments, thus front loading the project schedule.

Markov blankets and meta-heuristics search: sentiment extraction from unstructured texts

Extracting sentiments from unstructured text has emerged as an important problem in many disciplines. An accurate method would enable us, for example, to mine online opinions from the Internet and learn customers’ preferences for economic or marketing research, or for leveraging a strategic advantage. In this paper, we propose a two-stage Bayesian algorithm that is able to capture the dependencies among words, and, at the same time, finds a vocabulary that is efficient for the purpose of extracting sentiments. Experimental results on online movie reviews and online news show that our algorithm is able to select a parsimonious feature set with substantially fewer predictor variables than in the full data set and leads to better predictions about sentiment orientations than several state-of-the-art machine learning methods. Our findings suggest that sentiments are captured by conditional dependence relations among words, rather than by keywords or high-frequency words.

The diffusion of information technology among health maintenance organizations.

This article examines the information technology functions, staffing and cost, services provided, and advanced technologies among health maintenance organizations (HMOs) using a national sample of HMOs from mid-1995. HMOs have a well-developed capability to use data from administrative functions, such as claims processing. Nationally affiliated HMOs and HMOs in markets with greater HMO penetration support more IT functions. Relatively little work has been completed integrating clinical with administrative systems.

Evaluation of Healthcare IT Applications: The User Acceptance Perspective

As healthcare costs continue to spiral upward, healthcare institutions are under enormous pressure to create cost efficient systems without risking quality of care. Healthcare IT applications provide considerable promises for achieving this multifaceted goal through managing inofrmation, reducing costs, and facilitating total quality management and continuous quality improvement programs. However, the desired outcome can not be achieved if these applications are not being used. In order to better predict, explain, and increase the usage of IT, it is of vital importance to understand the antecedents of end users’ IT adoption decisions. This chapter first reviews the theoretical background of intention models that have been widely used to study factors governing IT acceptance, with particular focus on the technology acceptance model (TAM)—a prevalent technology adoption theory in the area of information system research. Although TAM has been extensively tested and shown to be a robust, powerful, and parsimonious model, its limitations have also been recognized. The second part of this chapter analyzes these limitations and discusses possible precautions of potential pitfalls. The third part of this chapter specifically addresses the applicability of the technology acceptance model in the professional context of physicians, with a review of available studies that have applied TAM to the technology adoption issues in healthcare.