Over the last years, scientific workflows have become mature enough to be used in a production style. However, despite the increasing maturity, there is still a shortage of tools for searching, adapting, and reusing workflows that hinders a more generalized adoption by the scientific communities. Indeed, due to the limited availability of machine-readable scientific metadata and the heterogeneity of workflow specification formats and representations, new ways to leverage alternative sources of information that complement existing approaches are needed. In this paper we address such limitations by applying statistically enriched generalized trie structures to exploit workflow execution provenance information in order to assist the analysis, indexing and search of scientific workflows. Our method bridges the gap between the description of what a workflow is supposed to do according to its specification and related metadata and what it actually does as recorded in its provenance execution trace. In doing so, we also prove that the proposed method outperforms SPARQL 1.1 Property Paths for querying provenance graphs.
Alternatively, we consider a workflow as a set of linked processes that can be analyzed in order to obtain a deeper knowledge of its execution, behavior and objectives. There are some ongoing research initiatives that follow this approach, in an attempt to extract patterns by automatically learning from the interlinked structures. @cite_7 the authors refer to this process as link pattern discovery being its main goal the characterization of interlinked relations which may or may not be defined explicitly. Also, at @cite_4 @cite_23 the authors referred to the term link mining in order to indicate how data mining techniques can explicitly consider the links between objects when building predictive or descriptive models of linked data. Notice that the analysis of interlinked structures are also applicable to other relevant open datasets such as DBpedia http: dbpedia.org .
@cite_23 @cite_4 @cite_7
1994432310 2404233270 1824264389 2036514379
It is known that productive pattern discovery from data has to interactively involve the user as directly as possible. State-of-the-art toolboxes require the specification of sophisticated workflows with an explicit selection of a data mining method, all its required parameters, and a corresponding algorithm. This hinders the desired rapid interaction---especially with users that are experts of the data domain rather than data mining experts. In this paper, we present a fundamentally new approach towards user involvement that relies exclusively on the implicit feedback available from the natural analysis behavior of the user, and at the same time allows the user to work with a multitude of pattern classes and discovery algorithms simultaneously without even knowing the details of each algorithm. To achieve this goal, we are relying on a recently proposed co-active learning model and a special feature representation of patterns to arrive at an adaptively tuned user interestingness model. At the same time, we propose an adaptive time-allocation strategy to distribute computation time among a set of underlying mining algorithms. We describe the technical details of our approach, present the user interface for gathering implicit feedback, and provide preliminary evaluation results. Many data sets can be represented naturally as collections of linked objects. For example, document collections can be represented as documents (nodes) connected by citations and hypertext references (links). Similarly, organizations can be represented as people (nodes) connected by reporting relationships, social relationships, and communication patterns (links). This type of data representation is facilitated by the growing availability of object-oriented databases and hypertext systems. The analysis of such data, often called link analysis, is becoming increasingly important in such diverse fields as law enforcement, fraud detection, epidemiology, and information retrieval. One important class of techniques for link analysis involves inductive inference — the construction of predictive relationships from linked data. For example, researchers in information retrieval might construct models to predict whether a particular WWW document is a homepage based on features of other documents to which it is connected (e.g., pages listing publications or family photos). Similarly, law enforcement agents might construct models to predict whether a person is a potential money-launderer based on bank deposits, international travel, business connections, and the arrest records of known associates. Constructing such predictive relationships is an essential part of link analysis. Despite growing data analysis opportunities, relatively little work examines the unique statistical challenges of inductive inference in linked data. This paper examines three such challenges: 1) statistical dependence caused by linked instances; 2) bias introduced by sampling density; and 3) multiple comparisons intensified by feature combinatorics. In general, current systems Process mining refers to the extraction of process models from event logs. Real-life processes tend to be less structured and more flexible. Traditional process mining algorithms have problems dealing with such unstructured processes and generate spaghetti-like process models that are hard to comprehend. One reason for such a result can be attributed to constructing process models from raw traces without due pre-processing. In an event log, there can be instances where the system is subjected to similar execution patterns behavior. Discovery of common patterns of invocation of activities in traces (beyond the immediate succession relation) can help in improving the discovery of process models and can assist in defining the conceptual relationship between the tasks activities. In this paper, we characterize and explore the manifestation of commonly used process model constructs in the event log and adopt pattern definitions that capture these manifestations, and propose a means to form abstractions over these patterns. We also propose an iterative method of transformation of traces which can be applied as a pre-processing step for most of today's process mining techniques . The proposed approaches are shown to identify promising patterns and conceptually-valid abstractions on a real-life log. The patterns discussed in this paper have multiple applications such as trace clustering, fault diagnosis anomaly detection besides being an enabler for hierarchical process discovery. Discovering patterns in a sequence is an important aspect of data mining. One popular choice of such patterns are episodes, patterns in sequential data describing events that often occur in the vicinity of each other. Episodes also enforce in which order the events are allowed to occur. In this work we introduce a technique for discovering closed episodes. Adopting existing approaches for discovering traditional patterns, such as closed itemsets, to episodes is not straightforward. First of all, we cannot define a unique closure based on frequency because an episode may have several closed superepisodes. Moreover, to define a closedness concept for episodes we need a subset relationship between episodes, which is not trivial to define. We approach these problems by introducing strict episodes. We argue that this class is general enough, and at the same time we are able to define a natural subset relationship within it and use it efficiently. In order to mine closed episodes we define an auxiliary closure operator. We show that this closure satisfies the needed properties so that we can use the existing framework for mining closed patterns. Discovering the true closed episodes can be done as a post-processing step. We combine these observations into an efficient mining algorithm and demonstrate empirically its performance in practice.