The harvesting, management, and analysis of thematic document collections is a major challenge in a wide variety of applications. While the criteria for compiling such collections are individual, the entire process is largely standardized. Therefore, it is not efficient to build new systems over and over again to take over these tasks. In this work, we introduce Seer-Dock, a novel and easy-to deploy general-purpose dockerized framework to build a scholarly document harvesting and management system. It is based on CiteSeerX, the most widely used scholarly search engine. Seer-Dock uses docker containers for all components and thus enables its users to rapidly deploy a full-fledged document collection and management system on any operating system platform and tailor it to the specific needs of an application domain. Moreover, it is easy to scale, orchestrate, maintain, and recover. In this resource paper, we introduce the architecture of Seer-Dock and its components. Like its kernel CiteSeerX, Seer-Dock is available under an Apache 2 open source license.

In this paper, a parallel dynamic load balancing algorithm; which offers a good trade-off between the balancing degree and communication costs; is proposed. The proposed algorithm is analyzed, investigated, and implemented. Also, a new mathematical solution for that algorithm is presented. The analytical solution is tested on standard task graphs (STG) as common test-bed benchmarks. All the different parameters concerned with processors, memory, interconnection networks, and task programs are also presented. The new proposed algorithm is compared and evaluated with a traditional dynamic load balancing one. Experimental results are presented by considering a set of criteria for evaluating the overall performance. Simulation and analytical results proved that the proposed algorithm outperforms the chosen one.

To achieve efficient and good performance of logic programs a proposed parallel execution model based on MUSE which was implemented by SICS of Sweden has been developed and implemented. This model is an amalgamation of two main types of parallelism AND-parallelism and OR-parallelism. The implementation of this model is done on a shared memory multiprocessor. Due to this model the logic program can be described by an AND/OR tree. The different branches of this tree are distributed to the available processing elements (PEs) during run time. Two scheduling techniques have been developed and implemented. In the first scheduler. The PEs are distributed through the AND/OR tree based on the sequential PROLOG (i.e. left-right depth first). The second scheduler is based on the static processing element allocation. Two benchmark programs have been used to assess the performance of this execution model; these are the natural language processing parser and an animal recognition expert system.>