What Automated Planning Can Do for Business Process Management

Business Process Management (BPM) is a central element of today organizations. Despite over the years its main focus has been the support of processes in highly controlled domains, nowadays many domains of interest to the BPM community are characterized by ever-changing requirements, unpredictable environments and increasing amounts of data that influence the execution of process instances. Under such dynamic conditions, BPM systems must increase their level of automation to provide the reactivity and flexibility necessary for process management. On the other hand, the Artificial Intelligence (AI) community has concentrated its efforts on investigating dynamic domains that involve active control of computational entities and physical devices (e.g., robots, software agents, etc.). In this context, Automated Planning, which is one of the oldest areas in AI, is conceived as a model-based approach to synthesize autonomous behaviours in automated way from a model. In this paper, we discuss how automated planning techniques can be leveraged to enable new levels of automation and support for business processing, and we show some concrete examples of their successful application to the different stages of the BPM life cycle

Biomedical ontology alignment: An approach based on representation learning

While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results

Mathematical modelling of anisotropy in fibrous connective tissue

We present two modelling frameworks for studying dynamic anistropy in connective tissue, motivated by the problem of fibre alignment in wound healing. The first model is a system of partial differential equations operating on a macroscopic scale. We show that a model consisting of a single extracellular matrix material aligned by fibroblasts via flux and stress exhibits behaviour that is incompatible with experimental observations. We extend the model to two matrix types and show that the results of this extended model are robust and consistent with experiment. The second model represents cells as discrete objects in a continuum of ECM. We show that this model predicts patterns of alignment on macroscopic length scales that are lost in a continuum model of the cell population

Statistical alignment in transfer learning to address the repair problem: An experimental case study

Repair is a critical step in maintenance of civil structures to ensure safe operation. However, repair can pose a problem for data-driven approaches of long-term structural health monitoring, because repairs can change the underlying distributions of the data, which can invalidate models trained on pre-repair data. As a result, models previously trained on pre-repair information fail to generalise to post-repair data, reducing their performances and misrepresenting the actual behaviour of structures. This paper suggests a population-based structural health monitoring approach to address the problem of repair in long-term monitoring of a mast structure, by exploring domain adaptation techniques developed for transfer learning. A combined approach of normal condition alignment and Dirichlet process mixture models is adopted here for damage detection, that can operate unimpeded by post-repair shifts in distributions. The method is able correctly identify 99\% of the damage data with a false positive rate of around 1.6%. Moreover, it is able to detect environmental variations such as stiffening due to freezing conditions that can adversely affect the dynamic behaviour of structures

Hybrid Bayesian Eigenobjects: Combining Linear Subspace and Deep Network Methods for 3D Robot Vision

We introduce Hybrid Bayesian Eigenobjects (HBEOs), a novel representation for 3D objects designed to allow a robot to jointly estimate the pose, class, and full 3D geometry of a novel object observed from a single viewpoint in a single practical framework. By combining both linear subspace methods and deep convolutional prediction, HBEOs efficiently learn nonlinear object representations without directly regressing into high-dimensional space. HBEOs also remove the onerous and generally impractical necessity of input data voxelization prior to inference. We experimentally evaluate the suitability of HBEOs to the challenging task of joint pose, class, and shape inference on novel objects and show that, compared to preceding work, HBEOs offer dramatically improved performance in all three tasks along with several orders of magnitude faster runtime performance.Comment: To appear in the International Conference on Intelligent Robots (IROS) - Madrid, 201

Conformance checking in UML artifact-centric business process models

Business artifacts have appeared as a new paradigm to capture the information required for the complete execution and reasoning of a business process. Likewise, conformance checking is gaining popularity as a crucial technique that enables evaluating whether recorded executions of a process match its corresponding model. In this paper, conformance checking techniques are incorporated into a general framework to specify business artifacts. By relying on the expressive power of an artifact-centric specification, BAUML, which combines UML state and activity diagrams (among others), the problem of conformance checking can be mapped into the Petri net formalism and its results be explained in terms of the original artifact-centric specification. In contrast to most existing approaches, ours incorporates data constraints into the Petri nets, thus achieving conformance results which are more precise. We have also implemented a plug-in, within the ProM framework, which is able to translate a BAUML into a Petri net to perform conformance checking. This shows the feasibility of our approach.Peer ReviewedPostprint (author's final draft

Repairing Alignments of Process Models

Process mining represents a collection of data driven techniques that support the analysis, understanding and improvement of business processes. A core branch of process mining is conformance checking, i.e., assessing to what extent a business process model conforms to observed business process execution data. Alignments are the de facto standard instrument to compute such conformance statistics. However, computing alignments is a combinatorial problem and hence extremely costly. At the same time, many process models share a similar structure and/or a great deal of behavior. For collections of such models, computing alignments from scratch is inefficient, since large parts of the alignments are likely to be the same. This paper presents a technique that exploits process model similarity and repairs existing alignments by updating those parts that do not fit a given process model. The technique effectively reduces the size of the combinatorial alignment problem, and hence decreases computation time significantly. Moreover, the potential loss of optimality is limited and stays within acceptable bounds

Infrastructure Asset Management Modeling through an Analysis of the Air Force Strategic Vision and Goals

Effective asset management requires an overarching model that establishes a framework for decision-makers. This research project develops a strategic level asset management model for varying types of infrastructure that provides guidance for effective asset management. The strategic model also incorporates Next Generation Information Technology initiatives into a single coherent system to streamline the top-down, bottom-up flow of information. The strategic model is applicable to agencies with a large, varying infrastructure inventory and limited resources. This research also develops an improved performance modeling tool, a critical component of the strategic model. This tool objectively prioritizes maintenance and repair projects according to measurable metrics as well as the strategic vision, established goals, and policies. Asset management of Air Force infrastructure provides an example of applicability for this strategic model and improved tool; thus, an asset management example of Air Force infrastructure is utilized throughout the research project to demonstrate the utility of the model and the tool. The strategic level model and improved tool enable policy-makers to make decisions that tie goals, infrastructure inventory, condition state, importance and criticality, and budget constraints to system performance. As a result, insight is gained on ways to maximize efficiency and optimize the performance of infrastructure

Economic development as design: Insight and guidance through the PSI framework

Economic development is aimed at improving the lives of people in the developing world, and needs to be carried out with design at its heart, but this has often not been the case. This paper first reviews dominant approaches to economic development including the use of subsidies or the creation of markets and demand and the testing of initiatives using randomized control trials. It then introduces ‘development engineering’ as a representative engineering design approach to engineering and technology in development before presenting the view that successful development needs to involve continual learning through innovation in context. The PSI (problem social institutional) framework is presented as a basis for guiding such development as a design activity, and its application is illustrated using examples from India of the unsuccessful introduction of new cooking stoves and then both successful and unsuccessful approaches to rural electrification. A 2-level approach to PSI is taken, in which the lower level represents daily operation of communities and the 2nd level represents the development project including addressing misalignments between the different PSI spaces and levels