Srividya Gopalan

Hybrid context model based on multilevel situation theory and ontology for contact centers

Context awareness and context modeling have raised their call for attention and importance quite recently in the world of contact centers for better customer services and relations. To address the issues of contextual reuse and contextual inconsistencies, there is a need for context models that foster context reuse and support ease of retrieving right kind of information by providing appropriate abstractions of contextual information. In this paper, we propose a hybrid context model based on multilevel situation theory and ontology. Multilevel situation theory is based on an intermediate level micro situation that is used to create entity specific situations. Such micro situations are used to enhance a situation so as to enable consistent contextual reasoning. The context model is hybrid in the sense that the proposed multilevel situation theory is defined based on the entities, entity attributes, and entity relationships as described in the contact center ontology. We also present an architectural overview of context aware processing system to build a rapid prototype that demonstrates the hybrid context model.

Context-aware Timely Information Delivery in Mobile Environments

In mobile environments, transmitting information relevant to an event along with notification of the event has been proven to be an effective means of providing revenue enhancing services. For example, a relevant advertisement can be displayed just before event notification; for instance, a product promotion by Beckham can be shown just before the notification of a goal scored by him. Challenges in achieving real-time search and delivery of information relevant to events as they occur include: (i) predicting the next event so that the appropriate information can be kept ready; (ii) finding information relevant to the context and content of the event; (iii) searching for and bringing the potentially needed information closer to the user location; and (iv) disseminating and displaying relevant information just before the actual event is notified to the user. In this paper, we propose a real-time information delivery system based on an events context and content. The key features of our system are: (i) representing domains with event-based scenarios using statecharts; (ii) using a novel combination of history information and state transitions to predict events; and (iii) real-time delivery of information relevant to an event by prefetching and caching information based on the events predicted to occur in the near future. To illustrate our approach, we focus on the delivery of advertisements relevant to notified events as a specific case of delivery of information relevant to the events. Using an experimental setup, we measured the effectiveness of our approach as compared to a context-unaware approach to event prediction. Experiments demonstrate that our approach has a superior performance, resulting in: (i) better prediction accuracy; (ii) the delivery of the most relevant information most of the time; and (iii) an effective cache management in mobile devices.

A distributed service discovery and selection framework in pervasive service environments

A growing number of inexpensive portable computing devices are becoming available that can access data and media services across heterogeneous networks and network standards. Concurrently, significant computational resources are being deployed within the network infrastructure, and this computational infrastructure is being used to offer diverse and innovative services to users of these networks. In such a type of pervasive environment, one of the critical issues for a mobile user would be locating an appropriate network, and discovering and selecting appropriate services. In recognition of this, we propose a novel distributed service discovery and selection framework that consists of service clustered servers (SCS) that are distributed across the network and form a service overlay network (SoN) to provide seamless service discovery and access. The SCS maintains a dynamic hierarchical cluster of services, while applying case based reasoning (CBR) techniques to provide proactive service selection for the roaming and fixed user to access these services.

Web-Based Context Aware Information Retrieval in Contact Centers

One of the important challenges in todays contact center solutions is to increase the speed at which the agents can find information to respond to customer queries. In this paper, we study the issues relevant to information retrieval by contact center agents and propose a solution to address those issues. Our proposed solution is towards: (a) defining user specific, agent specific, and business specific contexts for contact center; (b) using simple mechanisms initially to derive current context of a query and pre-fetch the information and appropriately present the pre-fetched information to the agent; (c) derive contact center specific analyzed context based on past information about customer, agent and business scenarios to understand the context of the query; and (d) using the current and analyzed contexts to retrieve information.

A Distributed Service Discovery Architecture for Pervasive Environments

A growing number of inexpensive portable computing devices are becoming available that can access data and media services across heterogeneous networks and network standards. Concurrently, significant computational resources are being deployed offering diverse and innovative services to users of these networks. In such type of a pervasive environment, one of the critical issues for a mobile user would be locating an appropriate network, discovering and selecting appropriate services. In recognition of this, we propose a novel distributed service discovery architecture that consists of service clustered servers (SCS) distributed across the network and form a service overlay network (SoN) to provide seamless service discovery and access. The architecture is extended to address wide area service using multi-tier model with facilitators. The real time implementation of architecture is performed and parameters such as latency to find a service, precision, CBR and gain in wide area networks are measured and discussed

CoS Based LSP Selection in MPLS Networks

Multi protocol Label Switching (MPLS) protocol is a network layer packet forwarding technology that provides flexible circuit switched traffic engineering solutions in packet switched networks by explicit path routing. They are an elegant solution to meet the Bandwidth management and QOS requirements of next generation IP-based backbone networks. One of the critical research issues in MPLS is Traffic engineering i.e., efficient mapping of the traffic flows on to the network and in effect enable QoS routing. In this paper, we propose a four-stage mapping of the incoming IP packet to the label in the control plane for creation of sub-FEC and sub-Label Switched Path (LSP), and CoS Based Lsp Selection (CBLS) algorithm in the data plane for dynamic selection of sub-LSP. This combined approach provides a circuit switched type of a service in a packet switched network for real-time applications like voice. We have evaluated the approach using ns2.26 and initial results are encouraging.

Multi-Attribute Based Similar Content Indexing in Hybrid Peer-to-Peer Networks

Peer-to-peer (P2P) networks are increasingly gaining acceptance over the Internet as they provide an infrastructure for content search and exchange. Hybrid P2P architecture is used to provide directory services for network clusters to share resources between peers within and across clusters. In this paper, we propose an advanced multi-attribute based content indexing scheme in hybrid P2P networks. The proposed indexing scheme is present only at the super peer (SP) to enable an intelligent controlled query routing mechanism for better bandwidth management and search precision in P2P networks. The proposed content indexing scheme builds attribute specific content indices to search for similar information content for a set of multiple content attributes. The resulting controlled query propagation using multi-attribute index tables at SP avoids flooding of queries, improves search efficiency and reduces query response time as compared to flooding and caching

Inter-frame similarity based video transcoding

Multimedia services over wireless networks were made popular by the arrival of smart handhelds. These devices bring heterogeneity to the wireless networks and to the content creation as the content cannot be delivered in its original format due to the difference in the capabilities of these handhelds. An intermediate stage of processing like transcoding is carried out before delivering multimedia content. Under certain scenarios, it is required to convert the content attributes such as bit rate, frame rate, etc. while still retaining the content format to cater to devices with varying capabilities. We propose an algorithm that prioritizes frames taking into account inter frame similarity to perform frame dropping. The priority value based frame dropping in turn aids in delivering better quality video. Our frame priority assignment algorithm is based on uniform distribution of dropped frames to minimize jitter and maximizing the distance between two consecutive dropped frames.

Hardware-software hybrid packet processing for intrusion detection systems

Security is a major issue in today’s communication networks. Designing Network Intrusion Detection systems (NIDS) calls for high performance circuits in order to keep up with the rising data rates. Offloading software processing to hardware realizations is not an economically viable solution and hence hardware-software based hybrid solutions for the NIDS scenario are discussed in literature. By deploying processing on both hardware and software cores simultaneously by using a novel Intelligent Rule Parsing algorithm, we aim to minimize the number of packets whose waiting time is greater than a predefined threshold. This fairness criterion implicitly ensures in obtaining a higher throughput as depicted by our results.

Priority-based high-speed intelligent rule-checking

Snort rule-checking is one of the most popular forms of network intrusion detection systems (NIDS). Recent work in string matching has focused on offloading string matching to hardware realizations in order to achieve time and space efficiencies. However, any form of implementation would have to maintain a threshold performance in order to keep with the packet data rate. While trying to maintain this threshold, control-unit is forced to either drop some packets or some rules. In this work, we present a packet priority technique that prioritizes the packets such that there is a high probability that the potentially malicious packets are detected within the threshold limit. We also present a novel intelligent string arrangement (ISA) that enables complete packet scanning in a smart and time efficient manner