Showing papers by "Sameep Mehta published in 2009"

Analyses for service interaction networks with applications to service delivery

Abstract: One of the distinguishing features of the services industry is the high emphasis on people interacting with other people and serving customers rather than transforming physical goods like in the traditional manufacturing processes. It is evident that analysis of such interactions is an essential aspect of designing effective and efficient services delivery. In this work we focus on learning individual and team behavior of different people or agents of a service organization by studying the patterns and outcomes of historical interactions. For each past interaction, we assume that only the list of participants and an outcome indicating the overall effectiveness of the interaction are known. Note that this offers limited information on the mutual (pairwise) compatibility of different participants. We develop the notion of service interaction networks which is an abstraction of the historical data and allows one to cast practical problems in a formal setting. We identify the unique characteristics of analyzing service interaction networks when compared to traditional analyses considered in social network analysis and establish a need for new modeling and algorithmic techniques for such networks. On the algorithmic front, we develop new algorithms to infer attributes of agents individually and in team settings. Our first algorithm is based on a novel modification to the eigen-vector based centrality for ranking the agents and the second algorithm is an iterative update technique that can be applied for subsets of agents as well. One of the challenges of conducting research in this setting is the sensitive and proprietary nature of the data. Therefore, there is a need for a realistic simulator for studying service interaction networks. We present the initial version of our simulator that is geared to capture several characteristics of service interaction networks that arise in real-life.

Categorizing Concepts for Detecting Drifts in Stream

Abstract: Mining evolving data streams for concept drifts has gained importance in applications like customer behavior analysis, network intrusion detection, credit card fraud detection. Several approaches have been proposed for detection of concept drifts in the context of supervised learning in data streams. Recently, researchers have been looking into the problem of identifying concept drifts in unlabeled data streams. Prevalent approaches study the evolution of streaming clusters using intrinsic and extrinsic characteristics of the discovered clusters, where each cluster is considered a concept. In this paper we model an unlabeled, uniform data stream as a stochastic poisson process and study the arrival pattern of data points to analyse the nature of an evolving concept (cluster). Each concept is modeled as stochastic poisson process and is individually observed for arrival rates of the incoming data points. A random sample of arrival rates is collected for each concept and appropriate non-parametric tests are applied to infer the nature of evolution for the concept. Concept drift in the stream can be inferred by the overall behavior of the concepts. We also propose a taxonomy of various types of concept behaviors and inter-relation among them. Experiments have been performed to demonstrate feasibility, validity and scalability of the proposed method.

Towards Providing Value Based Differentiated QoS

Abstract: In this paper we present a model for delivering differentiated quality of service based on the value for the provider. The value for the provider is captured by taking into account the i) client value, ii) service value, and certain exogenous factors. We enumerate and articulate different dimensions which will affect these components. We present a couple of case studies to demonstrate the usefulness of the proposed model. The first case study is from Retail Banking based on our real life experience of deploying a service delivery system in a leading bank in India. The second case study is from a Business Process Outsourcing industry. We also define the notion of value plot to visualize the relative importance of customers from value perspective and highlight how the value plot can be used for both operational as well as strategic planning in service delivery.

Service Interaction Networks: Simulation and Applications

Abstract: Service Interaction Networks [7] are abstractions used in modeling and analyzing people-centric aspects of service delivery. They pose interesting challenges from modeling, algorithmicand knowledge mining viewpoints. Typically, the data needed for the construction of serviceinteraction networks is sensitive in nature and organizations may not readily share the data.Therefore, it is imperative that we build realistic simulations of service interaction networks soas to enable not only the development of novel analyses, but also to validate their applicability.Lack of benchmark datasets to compare against poses challnges in validating simulations. Wepresent a simulation tool designed to take into account the people-centric effects of servicedelivery and an interesting approach to validate it. We hightlight some novel analyses of serviceinteraction networks that require new modeling and algorithmic insights. 1 Introduction One of the distinguishing features of the services industry is the high emphasis on people interactingwith each other and serving customers rather than transforming physical goods in the process.This aspect of service delivery results in several unique characteristics of services as opposed toother industrial settings. Naturally, modeling and analyzing services and their delivery, taking intoaccount people-centric aspects is an important research direction.In a typical service delivery scenario, an organization consisting of a set of trained actors receivesa time stamped stream of service requests. Each request is serviced by a subset of the actors as perits requirements. It is natural to view this process in terms of an abstract network. The notion ofService Interaction Networks was introduced by Kameshwaran et al. [7] to help model and analyzewide range of problems that arise in such a setting. However, several reasons (elaborated later) makeit difficult to obtain the necessary real-life data, especially for the purpose of publicly disseminatedresearch. In this paper, we present a simulation platform that captures various people-centric issuesofservice delivery and can be used to generate instances that are likely toreflect real-life observations.We present some example analyses of service interaction networks that have interesting applicationsin service delivery.The central abstract construct in service delivery scenarios is the so-called process model. Itconsists of many atomic work units (specialized and well-specified). The process model connects thevarious different atomic units associated with the process so as to reflect the logical and precedenceconstraints, and communication requirements. Typically, skilled individuals or a team possessingan appropriate combination of skills is assigned to an atomic unit and we call them as agents oractors. As we conceive of the service requests as tasks to be performed, throughout the paper,we will refer to the process model of the request as task-graph. Abstractly, it consists of nodesthat represent specific, atomic work units that can be assigned to actors and edges that captureinteraction requirements between the actors assigned to the corresponding work units. In general,the edges could connect multiple work units, i.e, they could be hyperedges. A request, representedby its task-graph is executed by assigning actors to each of the nodes. This assignment takes intoaccount required skill sets, capacity and other specifications of the tasks. If we consider the set of allthe actors in the system and the interactions between them during the course of executing a largenumber of service requests, we get networks that capture the overall interaction patterns amongthem. Each request also has an overall outcome associated with it. Combinging the outcomeswith the interaction patterns gives rise to an enriched Service Interaction Network [7] that couldpotentially reveal critical insights on the service delivery.Ideally, instead of monitoring request level outcomes, one would like to further micro-monitorthe execution of task-graphs. But, there are several issues that make micro-monitoring difficult.Chief among them is that a subset of the actors (functionalities in other words) assigned to therequests could be independent service vecndors (ISVs) whose actions may not be monitored. Evenwithin an organization, such micro-monitoring could result in issues related to morale, trust betweencolleagues, and unfair competitions etc. Therefore, in most scenarios, only high-level monitoring,such as measuring the overall effectiveness of a request resolution, is carried out. We now discusssome scenarios in which notion of service interaction networks is helpful.

Towards simultaneously exploiting structure and outcomes in interaction networks for node ranking

Abstract: In this paper, we present algorithms for ranking nodes in interaction networks. Informally,they capture the patterns of historical interaction among the nodes and the associated out-comes. There exists a cardinal ranking over the set of outcomes, characterizing the order ofpreference. We argue that ranking of nodes should be influenced by both structural proper-ties of the networks and the outcome/value created by the interactions. The former aspect iswell studied in social network analysis and is accounted for, in various measures like centrality,reputation, influence etc. However, the latter aspect is largely unexplored. Our proposed algo-rithms simultaneously take into account both structural properties as well as the outcomes toassign ranks for the nodes. We develop a novel eigenvector-like computation that exploits thestructural influences, importance of value creation, and any exogenous information available tothe ranking system. We report experimental results on the IMDB dataset.

Tutorial: Exploring Interaction Networks for Services Industry.

Abstract: One of the distinguishing features of the service sector is high emphasis on people interacting with people and serving the customer rather than transforming physical goods in the process. In traditional manufacturing, the machines are characterized by their ability to do only prespecified set of tasks, with quantifiable and predictable productivity rates. These properties makes it is relatively easy to understand, model and analyze the interactions e.g. One machine of type X can process the output of three machines of type Y. However, People, the analogue of machines in service chains are characterized by, (i) unpredictable productivity rate (ii) ability to become proficient and diversified in skill-set with time. Hence, people to people interaction which is pervasive in services industry provides technical challenges from analysis, diagnostic and optimization purposes. It is evident that analysis of such interactions is an essential aspect of designing effective and efficient services delivery. The results of analysis can be used to handle various aspects, e.g., training, team building, risk management etc. Analysis of interaction networks extracted from the service operations is the focus of this tutorial. In many ways, interaction networks are similar to the well-studied social networks. Traditionally, social network analysis has been used to study structural properties of the networks and the positional properties of the individuals. However, from the perspective of interaction networks, it is important to derive information on effectiveness of the interactions and the process of effective team formations. When these objective are taken into account, a rich set of problems emerge, some of which are further generalizations of traditional analysis. Typically, solving these problems involves multidisciplinary approach as in understanding the constraints of the domain, import of mathematical analysis techniques and appropriate interpretation of the results.