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Development of a distributed knowledge-based system
This paper describes the development of a distributed knowledge-based system. A software system, namely Distributed Algorithmic and Rule-based Blackboard System (DARBS), was developed from its predecessor ARBS, which lacked the distributed computing feature. ARBS has been used in solving a number of engineering problems [1-3]. DARBS now utilises client/server technology. It consists of a centralised database server, called the 'Blackboard' and a number of Knowledge Source Clients (experts). It distributes the workload to a number of clients which are rule-based or other AI systems with specific knowledge in various areas. DARBS is being applied to automatic interpretation of non-destructive evaluation (NDE) data and control of plasma deposition processes
On the Distributed Complexity of Large-Scale Graph Computations
Motivated by the increasing need to understand the distributed algorithmic
foundations of large-scale graph computations, we study some fundamental graph
problems in a message-passing model for distributed computing where
machines jointly perform computations on graphs with nodes (typically, ). The input graph is assumed to be initially randomly partitioned among
the machines, a common implementation in many real-world systems.
Communication is point-to-point, and the goal is to minimize the number of
communication {\em rounds} of the computation.
Our main contribution is the {\em General Lower Bound Theorem}, a theorem
that can be used to show non-trivial lower bounds on the round complexity of
distributed large-scale data computations. The General Lower Bound Theorem is
established via an information-theoretic approach that relates the round
complexity to the minimal amount of information required by machines to solve
the problem. Our approach is generic and this theorem can be used in a
"cookbook" fashion to show distributed lower bounds in the context of several
problems, including non-graph problems. We present two applications by showing
(almost) tight lower bounds for the round complexity of two fundamental graph
problems, namely {\em PageRank computation} and {\em triangle enumeration}. Our
approach, as demonstrated in the case of PageRank, can yield tight lower bounds
for problems (including, and especially, under a stochastic partition of the
input) where communication complexity techniques are not obvious.
Our approach, as demonstrated in the case of triangle enumeration, can yield
stronger round lower bounds as well as message-round tradeoffs compared to
approaches that use communication complexity techniques
Scalable Construction of Text Indexes with Thrill
The suffix array is the key to efficient solutions for myriads of string processing problems in different application domains, like data compression, data mining, or bioinformatics. With the rapid growth of available data, suffix array construction algorithms have to be adapted to advanced computational models such as external memory and distributed computing. In this article, we present five suffix array construction algorithms utilizing the new algorithmic big data batch processing framework Thrill, which allows scalable processing of input sizes on distributed systems in orders of magnitude that have not been considered before
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