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E-contracts begin as legal documents and end up as processes that help organizations abide by legal rules while fulfilling contract terms. As contracts are complex, their deployment is predominantly established and fulfilled with significant human involvement. One of the key difficulties with any kind of contract processing is the legal ambiguity, which makes it difficult to address any violation of the contract terms. Thus, there is a need to track clauses for the contract activities under execution and violation of clauses. This necessitates deriving clause patterns from e-contract documents and map to their respective activities for further monitoring and fulfillment of e-contracts during their enactment. In this paper, we present a classification approach to extract clause patterns from e-contract documents. This is a challenging task as activities and clauses are mostly derived from both legal and business process driven contract knowledge.

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Social Networks are gaining importance due to their enablement of modeling various types of interactions among individuals, communities and organizations. Network Topologies play a major role in analyzing the social networks for a variety of business application scenarios such as finding influencers in product campaigning and virtual communities to recommend music downloads. Social networks are dynamic in nature and detection of topologies from these networks presents a host of new challenges. In this paper, we present approaches for topology discovery, particularly star, ring and mesh, based on the measures of network centrality. These approaches facilitate an efficient way of discovering topologies for analyzing large social networks. We also discuss experiments on DBLP dataset to show the viability of our proposed approach.