Abstract: Provided is a computer-implemented method to find similar sets to a selected query set within a collection of sets, wherein each set represents a process. Each set is transformed to a representation in a vector space. Moreover, each set comprises a prefix. The method comprises creating and storing a data structure representing an inverted metric index in a storage device. The similar sets to the selected query set are identified by a probing step followed by a candidate verifying step. In the probing step, the inverted metric index is filtered by means of a predefined subspace of the vector space. In the candidate verifying step, each set of the filtered inverted metric index is identified as a similar set if its distance value is smaller or equal to a predefined distance threshold value.

Abstract: Provided is a method for the set similarity join, wherein each set represents a process and each token represents a process step. The process comprises a series of process steps executed in at least one source computer system. Hence, similar sets represent similar processes within a collection of processes. The method is based on a two-level signature scheme. Having indexed the sets using a first signature into inverted lists, selected lists, in particular long lists, are reindexed using a second signature. As a result, the number of candidates and thus the number of required distance calculations can be effectively reduced. Its experimental evaluation has shown that the method consistently outperforms state-of-the-art algorithms on datasets with diverging characteristics, suggesting a stable solution for a wide range of applications.

Abstract: Provided is a method for the set similarity join, wherein each set represents a process and each token represents a process step. The process comprises a series of process steps executed in at least one source computer system. Hence, similar sets represent similar processes within a collection of processes. The method is based on a two-level signature scheme. Having indexed the sets using a first signature into inverted lists, selected lists, in particular long lists, are reindexed using a second signature. As a result, the number of candidates and thus the number of required distance calculations can be effectively reduced. Its experimental evaluation has shown that the method consistently outperforms state-of-the-art algorithms on datasets with diverging characteristics, suggesting a stable solution for a wide range of applications.

Abstract: Provided is a computer-implemented method for computing an index for a first density-based clustering of a collection of records, wherein the index is stored with a storage device. The index supports the extraction of exact clusterings for any selected threshold distance ?* less than or equal to a predefined threshold distance ? and a predefined number of records MinPts, which forms the pair of input parameters for which the index is computed.

Abstract: Provided is a computer-implemented method to extract a subgraph from a graph which is stored with a storage device without prior knowledge of the structure of the graph. The graph represents a network. The subgraph starts from at least one selected node into a selected direction. Each directed edge of the graph is composed of an outgoing edge which is connected to a start node and an incoming edge which is connected to an end node. The subgraph is extracted according to a traversal of the graph starting from the at least one selected node into the selected direction according to a predefined graph traversal protocol.

Abstract: Provided is a computer-implemented method to find similar sets to a selected query set within a collection of sets, wherein each set represents a process. Each set is transformed to a representation in a vector space. Moreover, each set comprises a prefix. The method comprises creating and storing a data structure representing an inverted metric index in a storage device. The similar sets to the selected query set are identified by a probing step followed by a candidate verifying step. In the probing step, the inverted metric index is filtered by means of a predefined subspace of the vector space. In the candidate verifying step, each set of the filtered inverted metric index is identified as a similar set if its distance value is smaller or equal to a predefined distance threshold value.