VALIDATING REQUIREMENTS SPECIFICATIONS STATED IN KNOWLEDGE REPRESENTATION LANGUAGE TEMPLAR

Techniques for analysis and validation of software requirements specifications written in the knowledge representation language Templar are presented. Templar specifications are analyzed in terms of ambiguity, non-minimality, contradiction, incompleteness, and redundancy. Since Templar is a powerful knowledge representation language supporting a rich set of modeling primitives, it is difficult to reason directly on Templar specifications. To solve this problem, Templar specifications are mapped into equivalent temporal logic programs which are analyzed in terms the criteria listed above. However, it is hard to reason about Templar specifications because some of the criteria cannot be formally proven, and the verification of other criteria constitute undecidable or intractable problems. To overcome these difficulties, we consider a set of tractable conditions for each criteria, which serve as "alarms" for the user. If a condition is violated then it means that the specification either definitely has or potentially can have a problem. Furthermore, the user is notified about the source and the nature of the problem in certain cases.Information Systems Working Papers Serie

The E-Butler Service, or Has the Age of Electronic Personal Decision Making Assistants Arrived?

This paper describes an Electronic Butler (or e-Butler) that provides a customer-centric personalized shopping services to its subscribers across a wide range of products. This service is provided by identifying individual customer's shopping needs from the comprehensive purchasing history of that person and providing purchasing recommendations or direct purchasing decisions for the customer. e- Butler service consists of two components -- the Personal Shopping Assistant (PSA) service that provides purchasing recommendations to the customer and the Magic Wand (MW) service that directly makes purchases it believes the customer needs without any prior consultations with the customer. In order to understand how PSA and MW services of e-Butler are related to the existing one-to-one marketing and recommender systems, a general framework classifying various personalized shopping services is presented that clearly delineates PSA and MW services from these existing systems. Moreover, the paper presents an architecture of the e-Butler service, explains what its business value is, discusses its feasibility, and describes what needs to be done to make it a successful service.Information Systems Working Papers Serie

ON PERIODICITY IN TEMPORAL DATABASES

The issue of periodicity is generally understood to be a desirable property of temporal data that should be supported by temporal database models and their query languages. Nevertheless, there has so far not been any systematic examination of how to incorporate this concept into a temporal DBMS. In this paper we describe two concepts of periodicity, which we call strong periodicity and near periodicity, and discuss how they capture formally two of the intuitive meanings of this term. We formally compare the expressive power of these two concepts, relate them to existing temporal query languages, and show how they can be incorporated into temporal relational database query languages, such as the proposed temporal extension to SQL, in a clean and straightforward manner.Information Systems Working Papers Serie

Models of Customer Behavior: From Populations to Individuals

There have been various claims made in the marketing community about the benefits of 1-to-1 marketing versus traditional customer segmentation approaches and how much they can improve understanding of customer behavior. However, few rigorous studies exist that systematically compare these approaches. In this paper, we conducted such a systematic study and compared the performance of aggregate, segmentation, and 1-to-1 marketing approaches across a broad range of experimental settings such as multiple segmentation levels, multiple real world marketing datasets, multiple dependent variables, different types of classifiers, different segmentation techniques, and different predictive measures. Our results show that, overall, 1-to-1 modeling significantly outperforms the aggregate approach among high-volume customers and is never worse than aggregate approach among low-volume customers in our experimental settings. Moreover, the best segmentation techniques tend to outperform 1-to-1 modeling among low-volume customers.Information Systems Working Papers Serie

ON THE EXPRESSIVE POWER OF INFINITE TEMPORAL DATABASES

We discuss different techniques for representing infinite temporal data. There are two basic approaches: A procedural description, as used in production systems, and represented, in this paper, by a version of Datalog. The second approach is a more declarative method, using some form of temporal logic programming. We examine several versions of each approach, and compare their expressive power, i.e., what temporal data each formalism can capture.Information Systems Working Papers Serie

The E-Butler Service, or Has the Age of Electronic Personal Decision Making Assistants Arrived?

This paper describes an Electronic Butler (or e-Butler) that provides a customer-centric personalized shopping services to its subscribers across a wide range of products. This service is provided by identifying individual customer's shopping needs from the comprehensive purchasing history of that person and providing purchasing recommendations or direct purchasing decisions for the customer. e- Butler service consists of two components -- the Personal Shopping Assistant (PSA) service that provides purchasing recommendations to the customer and the Magic Wand (MW) service that directly makes purchases it believes the customer needs without any prior consultations with the customer. In order to understand how PSA and MW services of e-Butler are related to the existing one-to-one marketing and recommender systems, a general framework classifying various personalized shopping services is presented that clearly delineates PSA and MW services from these existing systems. Moreover, the paper presents an architecture of the e-Butler service, explains what its business value is, discusses its feasibility, and describes what needs to be done to make it a successful service.Information Systems Working Papers Serie

TEMPLAR: A KNOWLEDGE-BASED LANGUAGE FOR SOFTWARE SPECIFICATIONS USING TEMPORAL LOGIC

A software specification language Templar is defined. The language is based on temporal logic and on the Activity-Event-Condition-Activity model of a rule which is an extension of the Event-Condition-Activity model in active databases. The language supports a rich set of modeling primitives, including rules, procedures, temporal logic operators, events, activities, hierarchical decomposition of activities, and parallelism, combined together in a coherent system. The development of the language was guided by the following objectives: specifications written in Templar should be easy for the non-computer oriented users to understand, should have formal syntax and semantics, and it should be easy to map them into a broad range of design specifications.Information Systems Working Papers Serie

IT Driven Automation: The New Wave

There has been much discussion in the press about productivity improvements that grew at an annual rate of 3.55% from 2000 to 2003 [BW04]. One of the sources of this productivity growth is automation. We have all witnessed numerous ways in which companies have automated their business processes over the past decade. As a recent example, The Dallas Morning News reports in [Baj04] how Atmos Energy, the Dallas-based gas company, is automating its gas meter reading capabilities by using wireless technologies and thus reducing its staff by 225 employees over the next five years. In this article, we will examine current trends in the technology-driven automation and will argue that we are still in the early stages of a new wave of automation that will profoundly affect the economy and will significantly contribute to the productivity growth over the next 10 â 15 years. Industrial automation is an old phenomenon that goes back to the Industrial Revolution when machines replaced physical labor on a massive scale. Automation profoundly affected manufacturing over the past 25 years when industrial robots replaced various manual jobs in different spheres of manufacturing, including automobiles, computers and telecommunication equipment. More recently, automation was primarily driven by IT. For example, toll booth collectors recently became victims of IT-based automation when some of them lost their jobs to EZ-Pass technologies. Similarly, 225 employees at Atmos Energy will lose their jobs within the next 5 years due to the advancements in wireless technologies [Baj04]. Also, many cashiers in department stores and supermarkets will soon lose their jobs because of the advancements of the RFID tag technologies. Most of the jobs lost to automation have been routine production jobs, according to the job classification proposed by Robert Reich in [Rei91]. The main characteristics of these jobs are repetitiveness and structuredness since they have well defined procedural job descriptions. Examples of these jobs include assembly line workers, foremen, data processors, and toll collectors. The routine production jobs have been replaced by mechanical, electrical and IT-driven machines, including industrial robots and wireless communication devices. In this article, we claim that the next waive of automation will affect not only routine production workers, but also what Reich calls symbolic-analytic workers [Rei91], such as engineers, office and knowledge workers, managers, educators, and other groups of âmind workers.â Although few of these jobs will be eliminated completely, many of the more routine tasks in these jobs will be delegated to âsmart machinesâ within the next 10 â 15 years, leading to major restructuring and consolidation of some of these jobs. This phenomenon is examined in the rest of this article.Information Systems Working Papers Serie

KNOWLEDGE DISCOVERY FROM DATABASES: THE NYU PROJECT

More and more application domains, from financial market analysis to weather prediction, from monitoring supermarket purchases to monitoring satellite images, are becomingly increasingly data-intensive. The result is massive databases that are growing at a rapid rate - it has been estimated that the worldâs electronic data almost doubles every year. With this rate of data explosion, there is a pressing need for computers to play an increasing role in analyzing these huge data repositories which are impossible to penetrate manually. The challenge is to ferret out the regularities in the data that will prove to be interesting to the user. A group in the Information Systems department at the NYU Business School has been working in this area for a number of years. The focus of our project is now on the discovery of patterns from time series data. In this paper we give an overview of the kinds of databases we are "miningâ and the kinds of temporal patterns and rules which we are attempting to discover. In the first phase of this research, we have developed a taxonomy of patterns as a way to organize our research agenda. We wish to share the taxonomy with the research community in the "knowledge discovery in databases" area since we have found it useful in classifying the universe of regularities or patterns into distinct types, that is, patterns which differ in terms of their structure and the amount 6f search effort required to find them. Although the primary focus of our project is on time series data, and the examples we will present are chosen from this arena, the taxonomy is general enough to apply to any type of data.Information Systems Working Papers Serie

Models of Customer Behavior: From Populations to Individuals

There have been various claims made in the marketing community about the benefits of 1-to-1 marketing versus traditional customer segmentation approaches and how much they can improve understanding of customer behavior. However, few rigorous studies exist that systematically compare these approaches. In this paper, we conducted such a systematic study and compared the performance of aggregate, segmentation, and 1-to-1 marketing approaches across a broad range of experimental settings such as multiple segmentation levels, multiple real world marketing datasets, multiple dependent variables, different types of classifiers, different segmentation techniques, and different predictive measures. Our results show that, overall, 1-to-1 modeling significantly outperforms the aggregate approach among high-volume customers and is never worse than aggregate approach among low-volume customers in our experimental settings. Moreover, the best segmentation techniques tend to outperform 1-to-1 modeling among low-volume customers.Information Systems Working Papers Serie