Magic sets and stratified databases

Chen, Yi Jen

doi:10.1002/(sici)1098-111x(199703)12:3<203::aid-int3>3.0.co;2-t

Cited by 8 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To protect the confidentiality of intensional policies, it is necessary to design a completely distributed goal evaluation algorithm that discloses as few information on intensional policies as possible. Since bottom-up approaches to goal evaluation [e.g., fixpoint semantics (Park 1969), magic templates (Ramakrishnan 1991) and magic sets (Chen 1997)] require knowledge of all the policy statements that depend on a given credential, they do not represent an applicable solution to our problem. Hence, a top-down approach to goal evaluation needs to be employed.…”

Section: Introductionmentioning

confidence: 99%

GEM: A distributed goal evaluation algorithm for trust management

Trivellato¹,

Zannone²,

Etalle³

2012

Theory and Practice of Logic Programming

View full text Add to dashboard Cite

Note: To appear in Theory and Practice of Logic Programming (TPLP). AbstractTrust management is an approach to access control in distributed systems where access decisions are based on policy statements issued by multiple principals and stored in a distributed manner. In trust management, the policy statements of a principal can refer to other principals' statements; thus, the process of evaluating an access request (i.e., a goal) consists of finding a "chain" of policy statements that allows the access to the requested resource. Most existing goal evaluation algorithms for trust management either rely on a centralized evaluation strategy, which consists of collecting all the relevant policy statements in a single location (and therefore they do not guarantee the confidentiality of intensional policies), or do not detect the termination of the computation (i.e., when all the answers of a goal are computed). In this paper we present GEM, a distributed goal evaluation algorithm for trust management systems that relies on function-free logic programming for the specification of policy statements. GEM detects termination in a completely distributed way without disclosing intensional policies, thereby preserving their confidentiality. We demonstrate that the algorithm terminates and is sound and complete with respect to the standard semantics for logic programs.

show abstract

Section: Introductionmentioning

confidence: 99%

GEM: A distributed goal evaluation algorithm for trust management

Trivellato¹,

Zannone²,

Etalle³

2012

Theory and Practice of Logic Programming

View full text Add to dashboard Cite

show abstract

“…The query time is O(1). Finally, deductive databases can be considered as a quite different extension to handle this problem [12,14,15].…”

Section: Introductionmentioning

confidence: 99%

Decomposing DAGs into spanning trees: A new way to compress transitive closures

Chen

2011

2011 IEEE 27th International Conference on Data Engineering

Self Cite

View full text Add to dashboard Cite

Let G(V, E) be a digraph (directed graph) with n nodes and e edges. Digraph G* = (V, E*) is the reflexive, transitive closure if (v, u) E* iff there is a path from v to u in G. Efficient storage of G* is important for supporting reachability queries which are not only common on graph databases, but also serve as fundamental operations used in many graph algorithms. A lot of strategies have been suggested based on the graph labeling, by which each node is assigned with certain labels such that the reachability of any two nodes through a path can be determined by their labels. Among them are interval labelling, chain decomposition, and 2-hop labeling. However, due to the very large size of many real world graphs, the computational cost and size of labels using existing methods would prove too expensive to be practical. In this paper, we propose a new approach to decompose a graph into a series of spanning trees which may share common edges, to transform a reachability query over a graph into a set of queries over trees. We demonstrate both analytically and empirically the efficiency and effectiveness of our method.

show abstract

“…An important matter of research in such systems is the efficient evaluation of recursive queries. Various strategies for processing recursive queries have been proposed (see [6], [7], [8], [9], [10], [11], [12], [19], [30]). These strategies include evaluation methods such as naive evaluation [6], [27], seminaive evaluation [2], query/subquery [31], RQA/ FQI [25], Henschen-Naqvi [20], and the methods used in compiling recursive queries [16], [17], [18], [19], [20].…”

Section: Introductionmentioning

confidence: 99%

On the graph traversal and linear binary-chain programs

Chen

2003

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

Grahne et al. have presented a graph algorithm for evaluating a subset of recursive queries [14], [15]. This method consists of two phases. In the first phase, the method transforms a linear binary-chain program into a set of equations over expressions containing predicate symbols. In the second phase, a graph is constructed from the equations and the answers are produced by traversing the relevant paths. Here, we describe a new algorithm which requires less time than Grahne's. The key idea of the improvement is to reduce the search space that will be traversed when a query is invoked. Furthermore, we speed up the evaluation of cyclic data by generating most answers directly in terms of the answers already found and the associated "path information" instead of traversing the corresponding paths as usual. In this way, our algorithm achieves a linear time complexity for both acyclic and cyclic data.

show abstract

Magic sets and stratified databases

Cited by 8 publications

References 16 publications

GEM: A distributed goal evaluation algorithm for trust management

GEM: A distributed goal evaluation algorithm for trust management

Decomposing DAGs into spanning trees: A new way to compress transitive closures

On the graph traversal and linear binary-chain programs

Contact Info

Product

Resources

About