V. S. Subrahmanian

DOGMA: A Disk-Oriented Graph Matching Algorithm for RDF Databases

RDF is an increasingly important paradigm for the representation of information on the Web. As RDF databases increase in size to approach tens of millions of triples, and as sophisticated graph matching queries expressible in languages like SPARQL become increasingly important, scalability becomes an issue. To date, there is no graph-based indexing method for RDF data where the index was designed in a way that makes it disk-resident. There is therefore a growing need for indexes that can operate efficiently when the index itself resides on disk. In this paper, we first propose the DOGMA index for fast subgraph matching on disk and then develop a basic algorithm to answer queries over this index. This algorithm is then significantly sped up via an optimized algorithm that uses efficient (but correct) pruning strategies when combined with two different extensions of the index. We have implemented a preliminary system and tested it against four existing RDF database systems developed by others. Our experiments show that our algorithm performs very well compared to these systems, with orders of magnitude improvements for complex graph queries.

Computing most probable worlds of action probabilistic logic programs: scalable estimation for 1030,000 worlds

The semantics of probabilistic logic programs (PLPs) is usually given through a possible worlds semantics. We propose a variant of PLPs called action probabilistic logic programs or -programs that use a two-sorted alphabet to describe the conditions under which certain real-world entities take certain actions. In such applications, worlds correspond to sets of actions these entities might take. Thus, there is a need to find the most probable world (MPW) for -programs. In contrast, past work on PLPs has primarily focused on the problem of entailment. This paper quickly presents the syntax and semantics of -programs and then shows a naive algorithm to solve the MPW problem using the linear program formulation commonly used for PLPs. As such linear programs have an exponential number of variables, we present two important new algorithms, called

How Dirty Is Your Relational Database? An Axiomatic Approach

\textsf{HOP}

A Scalable Framework for Modeling Competitive Diffusion in Social Networks

and

Fast Activity Detection: Indexing for Temporal Stochastic Automaton-Based Activity Models

\textsf{SemiHOP}

Moving Target Defense II

to solve the MPW problem exactly. Both these algorithms can significantly reduce the number of variables in the linear programs. Subsequently, we present a “binary” algorithm that applies a binary search style heuristic in conjunction with the Naive,

A Computationally-Enabled Analysis of Lashkar-e-Taiba Attacks in Jammu and Kashmir

\textsf{HOP}

Computing the fault tolerance of multi-agent deployment

and

Embedding forecast operators in databases

\textsf{SemiHOP}

Multimedia Social Networks for Cultural Heritage Applications: The GIVAS Project

algorithms to quickly find worlds that may not be “most probable.” We experimentally evaluate these algorithms both for accuracy (how much worse is the solution found by these heuristics in comparison to the exact solution) and for scalability (how long does it take to compute). We show that the results of