Nikolay Laptev

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Approximate results based on samples often provide the only way in which advanced analytical applications on very massive data sets can satisfy their time and resource constraints. Unfortunately, methods and tools for the computation of accurate early results are currently not supported in MapReduce-oriented systems although these are intended for 'big(More)
Digital signal processing applications often require the computation of linear systems. These computations can be considerably expensive and require optimizations for lower power consumption, higher throughput, and faster response time. Unfortunately, system designers do not have the necessary tools to take advantage of the wide flexibility in ways to(More)
—The problem of supporting data mining applications proved to be difficult for database management systems and it is now proving to be very challenging for data stream management systems (DSMSs), where the limitations of SQL are made even more severe by the requirements of continuous queries. The major technical advances that achieved separately on DSMSs(More)
— Approximate results based on samples often provide the only way in which advanced analytical applications on very massive data sets (a.k.a. 'big data') can satisfy their time and resource constraints. Unfortunately, methods and tools for the computation of accurate early results are currently not supported in big data systems (e.g., Hadoop). Therefore, we(More)
This paper introduces a generic and scalable framework for automated anomaly detection on large scale time-series data. Early detection of anomalies plays a key role in maintaining consistency of person's data and protects corporations against malicious attackers. Current state of the art anomaly detection approaches suffer from scalability, use-case(More)
Faced with the problem of characterizing systematic changes in multivariate time series in an unsupervised manner, we derive and test two methods of regularizing hidden Markov models for this task. Regularization on state transitions provides smooth transitioning among states, such that the sequences are split into broad, contiguous segments. Our methods(More)
— Matrix decomposition is required in various algorithms used in wireless communication applications. FPGAs strike a balance between ASICs and DSPs, as they have the programmability of software with performance capacity approaching that of a custom hardware implementation. However, FPGA architectures require designers to make a countless number of system,(More)
—Complex pattern queries play a critical role in many applications that must efficiently search databases and data streams. Current techniques support the search for multiple patterns using deterministic or non-deterministic automata. In practice however, the static pattern representation does not fully utilize available system resources, subsequently(More)