Conference Paper

Tackling the Partner Units Configuration Problem

DOI: 10.5591/978-1-57735-516-8/IJCAI11-091 Conference: IJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence, At Barcelona, Catalonia, Spain
Source: DBLP

ABSTRACT The Partner Units Problem is a specific type of configuration problem with important applications in the area of surveillance and security. In this work we show that a special case of the problem, that is of great interest to our partners in industry, can directly be tackled via a structural problem decompostion method. Combining these theoretical insights with general purpose AI techniques such as constraint satisfaction and SAT solving proves to be particularly effective in practice.

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Available from: Markus Aschinger, May 16, 2014
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    • "Factorised representations are a natural fit whenever we deal with a large space of possibilities and can be used to represent, e.g., AND/OR trees used in design specification [5] and world-set decompositions used for incomplete information [1]. They can also be used to compactly represent the space of feasible solutions to configuration problems in constraint satisfaction , where we need to connect a set of components so as to meet an objective while respecting given constraints [2]. The focus of this demonstration is our query engine FDB. "
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    ABSTRACT: FDB is an in-memory query engine for factorised databases, which are relational databases that use compact factorised representations at the physical layer to reduce data redundancy and boost query performance. We demonstrate FDB using real data sets from IMDB, DBLP, and the NELL repository of facts learned from Web pages. The users can inspect factorisations as well as plans used by FDB to compute factorised results of select-project-join queries on factorised databases.
    Proceedings of the VLDB Endowment 08/2012; 5(12):1950-1953. DOI:10.14778/2367502.2367545
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    • "Formalisms for incomplete information, such as world-set decompositions [4] [17], rely on factorisations of universal relations encoding very large sets of possible worlds; they are products of unions of products of tuples. Outside data management scenarios, factorised relations can be used to compactly represent the space of feasible solutions to configuration problems in constraint satisfaction, where we need to connect a fixed finite set of given components so as to meet a given objective while respecting given constraints [5]. Factorised representations have several key properties that make them appealing in the above mentioned scenarios. "
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    ABSTRACT: Factorised databases are relational databases that use compact factorised representations at the physical layer to reduce data redundancy and boost query performance. This paper introduces FDB, an in-memory query engine for select-project-join queries on factorised databases. Key components of FDB are novel algorithms for query optimisation and evaluation that exploit the succinctness brought by data factorisation. Experiments show that for data sets with many-to-many relationships FDB can outperform relational engines by orders of magnitude.
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    ABSTRACT: The Partner Units Problem (PUP) is a new benchmark configuration problem. This problem involves the configuration of a network of sensors and controllers, and has drawn significant attention due to the amount of industrial applications it finds. In this dissertation, we further explored previous work done on a tractable class of the problem, exploiting the notion of a path decomposition, representing and re-evaluating the encodings for the general version of the problem. During this endeavor, through constraint satisfaction methods, we presented new implied constraints and search conditions, which resulted in a number of results that give us new insight into the problem. Next, we extensively presented all the classes of the problem and analyzed their complexity, a problem that had been left open. Interestingly enough, the discrepancy between the classes of the problem was significant in terms of their structural properties. The complexity analysis showed that all the non trivial classes seem to belong in NP-Complete (some were proven and others were conjectured), and even the most trivial classes of the problem were proven to be solvable only in PTime. Finally, we presented a logical approach to the PUP; we used two algorithmic meta-theorems to approach and tackle the complexity of our unsolved classes. In doing so we discovered a new configuration problem, the Partner Units Embedding Problem, which we analyzed and proved to be fixed-point linear in respect to the treewidth of the input graphs.
    09/2011, Degree: MSc., Supervisor: G. Gottlob, C. Drescher
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