An adaptive technique for content-based image retrieval

We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search

Ontology-based end-user visual query formulation: Why, what, who, how, and which?

Value creation in an organisation is a time-sensitive and data-intensive process, yet it is often delayed and bounded by the reliance on IT experts extracting data for domain experts. Hence, there is a need for providing people who are not professional developers with the flexibility to pose relatively complex and ad hoc queries in an easy and intuitive way. In this respect, visual methods for query formulation undertake the challenge of making querying independent of users’ technical skills and the knowledge of the underlying textual query language and the structure of data. An ontology is more promising than the logical schema of the underlying data for guiding users in formulating queries, since it provides a richer vocabulary closer to the users’ understanding. However, on the one hand, today the most of world’s enterprise data reside in relational databases rather than triple stores, and on the other, visual query formulation has become more compelling due to ever-increasing data size and complexity—known as Big Data. This article presents and argues for ontology-based visual query formulation for end-users; discusses its feasibility in terms of ontology-based data access, which virtualises legacy relational databases as RDF, and the dimensions of Big Data; presents key conceptual aspects and dimensions, challenges, and requirements; and reviews, categorises, and discusses notable approaches and systems

A systematic approach for discovering causal dependencies between observations and incidents in the health and safety domain

The paper at hand motivates, proposes, demonstrates, and evaluates a novel systematic approach to discovering causal dependencies between events encoded in large arrays of data, called causality mining. The approach has emerged in the discussions with our project partner, an Australian public energy company. It was successfully evaluated in a case study with the project partner to extract valuable, and otherwise unknown, information on the causal dependencies between observations reported by the company’s employees as part of the organizational health and safety management practices and incidents that had occurred at the organization’s sites. The dependencies were derived based on the notion of proximity of the observations and incidents. The setup and results of the evaluation are reported in this paper. The new approach and the delivered insights aim at improving the overall health and safety culture of the project partner practices, as they can be applied to caution and, thus, prevent future incidents

Filtering out infrequent behavior from process event logs

In the era of “big data”, one of the key challenges is to analyze large amounts of data collected in meaningful and scalable ways. The field of process mining is concerned with the analysis of data that is of a particular nature, namely data that results from the execution of business processes. The analysis of such data can be negatively influenced by the presence of outliers, which reflect infrequent behavior or “noise”. In process discovery, where the objective is to automatically extract a process model from the data, this may result in rarely travelled pathways that clutter the process model. This paper presents an automated technique to the removal of infrequent behavior from event logs. The proposed technique is evaluated in detail and it is shown that its application in conjunction with certain existing process discovery algorithms significantly improves the quality of the discovered process models and that it scales well to large datasets

A toolkit for streaming process data analysis

This paper presents a software toolkit that can be used to analyze event data streams in real-time. It has a specific focus on stochastic analysis of business processes, based on event data that is produced during the execution of those processes. The toolkit provides a software environment that facilitates easy connection to event data streams and quick development and testing of analysis and visualization techniques. It is developed by classifying existing techniques for streaming process data analysis, which are identified in the current literature, and by extracting and formalizing the core mechanisms that these techniques are based on. These core mechanisms serve as the basis for the toolkit. The toolkit is implemented and made available as open source. In this way it can facilitate quick prototyping of streaming process data analysis techniques

Automated risk mitigation in business processes

This paper proposes a concrete approach for the automatic mitigation of risks that are detected during process enactment. Given a process model affected by risks, e.g. a financial process exposed to the risk of approval fraud, we enact this process and as soon as the likelihood of the associated risk(s) is no longer tolerable, we generate a set of possible mitigation actions to reduce the risks’ likelihood, ideally annulling the risks altogether. A mitigation action is a sequence of controlled changes applied to the running process instance, taking into account a snapshot of the process resources and data, and the current status of the system in which the process is executed. These actions are proposed as recommendations to help process administrators mitigate process-related risks as soon as they arise. The approach has been implemented in the YAWL environment and its performance evaluated. The results show that it is possible to mitigate process-related risk

Efficient querying of large process model repositories

Recent years have seen an increased uptake of business process management technology in industries. This has resulted in organizations trying to manage large collections of business process models. One of the challenges facing these organizations concerns the retrieval of models from large business process model repositories. For example, in some cases new process models may be derived from existing models, thus finding these models and adapting them may be more effective and less error-prone than developing them from scratch. Since process model repositories may be large, query evaluation may be time consuming. Hence, we investigate the use of indexes to speed up this evaluation process. To make our approach more applicable, we consider the semantic similarity between labels. Experiments are conducted to demonstrate that our approach is efficient

Synchronization and cancelation in workflows based on reset nets

Workflow languages offer constructs for coordinating tasks. Among these constructs are various types of splits and joins. One type of join, which shows up in various incarnations, is the OR-join. Different approaches assign a different (often only intuitive) semantics to this type of join, though they do share the common theme that branches that cannot complete will not be waited for. Many systems and languages struggle with the semantics and implementation of the OR-join because its non-local semantics require a synchronization depending on the analysis of future execution paths. The presence of cancelation features, potentially unbounded behavior, and other OR-joins in a workflow further complicates the formal semantics of the OR-join. In this paper, the concept of the OR-join is examined in detail in the context of the workflow language YAWL, a powerful workflow language designed to support a collection of workflow patterns and inspired by Petri nets. The paper provides a suitable (non-local) semantics for an OR-join and gives a concrete algorithm with two optimization techniques to support the implementation. This approach exploits a link that is proposed between YAWL and reset nets, a variant of Petri nets with a special type of arc that can remove all tokens from a place when its transition fires. Through the behavior of reset arcs, the behavior of cancelation regions can be captured in a natural manner

Epilogue

This book presents both the YAWL language and the supporting toolset. YAWL fits well in the transition from workflow management systems focusing on automation of structured processes to Business Process Management (BPM) systems aiming at a more comprehensive support of a wider variety of business processes. The development of YAWL was triggered by limitations of existing software for process automation. Although much has been written about business processes, there is a lack of consensus about how they are best described for the purposes of analysis and subsequent enactment. This has resulted in a plethora of approaches for capturing business processes. Despite an abundance of standardization initiatives, there is no common agreement on the essential concepts. Standardization processes are mainly driven by vested business interests. The large and influential software companies in the BPM area are involved in multiple standardization processes (to keep all options open), but do not wholeheartedly commit to any of them. The inherent complexity of business processes and the question of what fundamental concepts are necessary for business process modeling, enactment, and analysis gave rise to the development of a collection of workflow patterns. These patterns describe process modeling requirements in a language-independent manner. The Workflow Patterns Initiative started in the late nineties when the first set of 20 control-flow patterns were identified. These patterns where inspired by the design patterns for object-oriented design by Gamma et al. and focused on the ordering of tasks in business processes (e.g., parallelism, choice, synchronization, etc). Later this set of patterns was extended to more than 40 patterns. Moreover, driven by the success of the control-flow patterns, also other perspectives were analyzed using a patterns-based approach. This resulted in data patterns (dealing with the passing of information, scoping of variables, etc.), resource patterns (dealing with roles, task allocation, work distribution, delegation, etc.), exception handling patterns (dealing with exceptional situations), flexibility patterns (to make processes more adaptive and adaptable), interaction patterns (formodeling the glue between processes), and so on. All of these patterns resulted in a systematic overview of the possible, and maybe also expected, functionality of an ideal BPM solution

Workflow resource patterns

Workflow systems seek to provide an implementation vehicle for complex, recurring business processes. Notwithstanding this common objective, there are a variety of distinct features offered by commercial workflow management systems. These differences result in significant variations in the ability of distinct tools to represent and implement the plethora of requirements that may arise in contemporary business processes. Many of these requirements recur quite frequently during the requirements analysis activity for workflow systems and abstractions of these requirements serve as a useful means of identifying the key components of workflow languages. Previous work has identified a number of Workflow Control Patterns and Workflow Data Patterns, which characterize the range of control flow and data constructs that might be encountered when modelling and analysing workflows. In this paper, we describe a series of Workflow Resource Patterns that aim to capture the various ways in which resources are represented and utilized in workflows. By delineating these Patterns in a form that is independent of specific workflow technologies and modelling languages, we are able to provide a comprehensive treatment of the resource perspective and we subsequently use these Patterns as the basis for a detailed comparison of a number of commercially available workflow management systems and business process modelling languages