Kia Teymourian

Detection of stateful complex event patterns using parallel programming features is a challenging task because of statefulness of event detection operators. Parallelization of event detection tasks needs to be implemented in a way that keeps track of state changes by new arriving events. In this paper, we describe our implementation for a customized complex event detection engine by using Open Multi-Processing (OpenMP), a shared memory programming model. In our system event detection is implemented using Deterministic Finite Au-tomata (DFAs). We implemented a data stream aggregator that merges 4 given event streams into a sequence of C++ objects in a buffer used as source event stream for event detection in a next processing step. We describe implementation details and 3 architectural variations for stream aggregation and parallelized of event processing. We conducted performance experiments with each of the variations and report some of our experimental results. A comparison of our performance results shows that for event processing on single machine with multi cores and limited memory, using mutli-threads with shared buffer has better stream processing performance than an implementation with multi-processes and shared memory.

We describe an approach for a custom complex event processing engine using Message Passing Interface (MPI) in C++ programming language. Our approach utilizes a multi-processor infrastructure and distributes its load on multiple processes, expecting each process to run on one processor. A dispatching process receives events and distributes them on several query processes which are responsible for updating the actual queries. Query processes forwards any updates to a presentation process that output the results in an appropriate format. The distribution of roles among processes allows better scalability since further query processes can be added dynamically to handle more queries. In our evaluation we measured event processing up to a throughput of 12k events/sec using 4 processor cores.

Feature-based sentiment analysis can be realized on different types of object features. Some of these features might be about technical aspects of the objects and some others might be application-oriented features. The application-oriented features are more abstract features and can be of interest to the broad number of people than only the technical experts of specific products. In this paper, we propose an approach for extraction of abstract object features from a set of sub-features. In our approach we use a knowledge base about the application domain to extract related sub-features of high-level abstract features. On the basis of such related sub-features our approach performs the extraction of more abstract features that are only implicitly included in the analysis text.