Event Stream Processing with Multiple Threads

Current runtime verification tools seldom make use of multi-threading to speed up the evaluation of a property on a large event trace. In this paper, we present an extension to the BeepBeep 3 event stream engine that allows the use of multiple threads during the evaluation of a query. Various parallelization strategies are presented and described on simple examples. The implementation of these strategies is then evaluated empirically on a sample of problems. Compared to the previous, single-threaded version of the BeepBeep engine, the allocation of just a few threads to specific portions of a query provides dramatic improvement in terms of running time

Complex Methods and Class Allocation in Distributed OODBSs

In a distributed object-oriented database system (DODBS), queries which invoke methods executing at different sites and access different classes need to be executed very efficiently. Therefore, the methods invoked and classes accessed by the queries need to be allocated to sites so as to reduce the data transfer cost in processing a given set of queries. The methods and class allocation(MCA) problem needs to take into consideration complex interdependencies among queries, methods and classes. In this paper, we develop a comprehensive cost model for total data transfer incurred in processing a given set of queries by incorporating the dependencies among the queries, methods and classes. Further, we develop an iterative approach to generate near-optimal solution for the combined MCA problems by using the above cost model. In this approach, we start with an initial class allocation(CA) which is used for method allocation (MA), which in turn is used for CA, and so on. We stop this iterativ..

A Global Paradigm for Designing Parallel Relational Data Warehouses in Distributed Environments

Designing a Parallel Relational Data Warehouse (PRDW) consists of a set of tasks: (i) choosing the hardware architecture; (ii) fragmenting the data warehouse schema; (iii) allocating the generated fragments; (iv) replicating fragments in order to ensure high performance; (v) defining the strategies for load balancing and query processing. The major drawback of this life-cycle is the fact that it does not consider the inter-dependency among sub-problems related to the design of PRDW, and it makes use of heterogeneous metrics to evaluate the \u201cquality\u201d of the final design. In previous research efforts, we introduced an analytical cost model for parallel OLAP query processing in cluster environments. In a second experience, we have taken into account the inter-dependency existing between fragmentation and allocation. In this paper, we propose a novel methodology, called F&A&R, which further extends previous results, and defines an approach where the main PRDW design phases (i.e., fragmentation, allocation, and replication) are performed simultaneously, in a global fashion. In particular, our approach determines whether the fragmentation pattern currently generated is relevant to the allocation process or not. An original method of supporting data replication, based on fuzzy k-means clustering, is also proposed and successfully integrated within the whole design framework. Finally, we experimentally assessed the performance of F&A&R against a well-known data warehouse benchmark, with very promising results