Alexey Roytman

Automatic analysis of call-center conversations

We describe a system for automating call-center analysis and monitoring. Our system integrates transcription of incoming calls with analysis of their content; for the analysis, we introduce a novel method of estimating the domain-specific importance of conversation fragments, based on divergence of corpus statistics. Combining this method with Information Retrieval approaches, we provide knowledge-mining tools both for the call-center agents and for administrators of the center.

Defending financial infrastructures through early warning systems: the intelligence cloud approach

Recent evidence of successful Internet-based attacks and frauds involving financial institutions highlights the inadequacy of the existing protection mechanisms, in which each instutition implements its own isolated monitoring and reaction strategy. Analyzing on-line activity and detecting attacks on a large scale is an open issue due to the huge amounts of events that should be collected and processed. In this paper, we propose a large-scale distributed event processing system, called intelligence cloud, allowing the financial entities to participate in a widely distributed monitoring and detection effort through the exchange and processing of information locally available at each participating site. We expect this approach to be able to handle large amounts of events arriving at high rates from multiple domains of the financial scenario. We describe a framework based on the intelligence cloud where each participant can receive early alerts enabling them to deploy proactive countermeasures and mitigation strategies.

Bulletin board: a scalable and robust eventually consistent shared memory over a peer-to-peer overlay

We present the design and early experience with a completely new implementation of the Bulletin Board, a topicbased distributed shared memory service employed by commercial-grade application middleware, to achieve robustness and administrative simplicity with adequate latency and costs at the required throughput and scale. To facilitate scalability, only weak consistency is provided. For robustness and ease of use, the implementation is designed in a fully peer-to-peer fashion leveraging the weakly consistent group communication services provided by a semi-structured overlay network. We discuss issues in providing good (while not perfect) stability and reliability at tolerable cost. We address scalability issues, such as supporting large numbers of processes, large subscription spaces, and complex interest patterns. We also consider comprehensive API instrumentation.

Brief announcement: reconfigurable state machine replication from non-reconfigurable building blocks

Reconfigurable state machine replication is an important enabler of elasticity for replicated cloud services, which must be able to dynamically adjust their size as a function of changing load and resource availability. We introduce a new generic framework to allow the reconfigurable state machine implementation to be derived from a collection of arbitrary non-reconfigurable state machines. Our reduction framework follows the black box approach, and does not make any assumptions with respect to its execution environment apart from reliable channels. It allows higher-level services to leverage speculative command execution to ensure uninterrupted progress during the reconfiguration periods as well as in situations where failures prevent the reconfiguration agreement from being reached in a timely fashion. We apply our framework to obtain a reconfigurable speculative state machine from the non-reconfigurable Paxos implementation, and analyze its performance on a realistic distributed testbed. Our results show that our framework incurs negligible overheads in the absence of reconfiguration, and allows steady throughput to be maintained throughout the reconfiguration periods.