Exploring location-dependency in process modeling

Purpose – Context-awareness has emerged as an important principle in the design of flexible business processes. The goal of the research is to develop an approach to extend context-aware business process modeling toward location-awareness. The purpose of this paper is to identify and conceptualize location-dependencies in process modeling. Design/methodology/approach – This paper uses a pattern-based approach to identify location-dependency in process models. The authors design specifications for these patterns. The authors present illustrative examples and evaluate the identified patterns through a literature review of published process cases. Findings – This paper introduces location-awareness as a new perspective to extend context-awareness in BPM research, by introducing relevant location concepts such as location-awareness and location-dependencies. The authors identify five basic location-dependent control-flow patterns that can be captured in process models. And the authors identify location-dependencies in several existing case studies of business processes. Research limitations/implications – The authors focus exclusively on the control-flow perspective of process models. Further work needs to extend the research to address location-dependencies in process data or resources. Further empirical work is needed to explore determinants and consequences of the modeling of location-dependencies. Originality/value – As existing literature mostly focusses on the broad context of business process, location in process modeling still is treated as “second class citizen” in theory and in practice. This paper discusses the vital role of location-dependencies within business processes. The proposed five basic location-dependent control-flow patterns are novel and useful to explain location-dependency in business process models. They provide a conceptual basis for further exploration of location-awareness in the management of business processes.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

On Merging Business Process Management and Geographic Information Systems : Modeling and Execution of Ecological Concerns in Processes

Business Process Management describes a holistic management approach for the systematic design, modeling, execution, validation, monitoring and improvement of organizational business processes. Traditionally, most attention within this community has been given to control-flow aspects, i.e., the ordering and sequencing of business activities, oftentimes in isolation with regards to the context in which these activities occur. In this paper, we propose an approach that allows executable process models to be integrated with Geographic Information Systems. This approach enables process models to take geospatial and other geographic aspects into account in an explicit manner both during the modeling phase and the execution phase. We contribute a structured modeling methodology, based on the well-known Business Process Model and Notation standard, which is formalized by means of a mapping to executable Colored Petri nets. We illustrate the feasibility of our approach by means of a sustainability-focused case example of a process with important ecological concerns.

Understanding the behavior of workshop facilitators in systems analysis and design projects: Developing theory from process modeling projects

Organizational and technological systems analysis and design practices such as process modeling have received much attention in recent years. However, while knowledge about related artifacts such as models, tools, or grammars has substantially matured, little is known about the actual tasks and interaction activities that are conducted as part of analysis and design acts. In particular, key role of the facilitator has not been researched extensively to date. In this paper, we propose a new conceptual framework that can be used to examine facilitation behaviors in process modeling projects. The framework distinguishes four behavioral styles in facilitation (the driving engineer, the driving artist, the catalyzing engineer, and the catalyzing artist) that a facilitator can adopt. To distinguish between the four styles, we provide a set of ten behavioral anchors that underpin facilitation behaviors. We also report on a preliminary empirical exploration of our framework through interviews with experienced analysts in six modeling cases. Our research provides a conceptual foundation for an emerging theory for describing and explaining different behaviors associated with process modeling facilitation, provides first preliminary empirical results about facilitation in modeling projects, and provides a fertile basis for examining facilitation in other conceptual modeling activities.

Modeling of sonochemical decomposition of CCI4 in aqueous solutions

In the context of removal of organic pollutants from wastewater, sonolysis of CCl4 dissolved in water has been widely investigated. These investigations are either completely experimental or correlate data empirically. In this work, a quantitative model is developed to predict the rate of sonolysis of aqueous CCl4. The model considers the isothermal growth and partially adiabatic collapse of cavitation bubbles containing gas and vapor leading to conditions of high temperatures and pressures in them, attainment of thermodynamic equilibrium at the end of collapse, release of bubble contents into the liquid pool, and reactions in the well-mixed pool. The model successfully predicts the extent of degradation of dissolved CCl4, and the influence of various parameters such as initial concentration of CCl4, temperature, and nature of gas atmosphere above the liquid. in particular, it predicts the results of Hua and Hoffmann (Environ. Sci Technol, 1996, 30, 864-871), who found that degradation is first order with CCl4 and that Argon as well as Ar-O-3 atmospheres give the same results. The framework of the model is capable of quantitatively describing the degradation of many dissolved organics by considering all the involved species.

Modeling Asymmetries in Financial Data with Multiplicative Error Models

This thesis addresses modeling of financial time series, especially stock market returns and daily price ranges. Modeling data of this kind can be approached with so-called multiplicative error models (MEM). These models nest several well known time series models such as GARCH, ACD and CARR models. They are able to capture many well established features of financial time series including volatility clustering and leptokurtosis. In contrast to these phenomena, different kinds of asymmetries have received relatively little attention in the existing literature. In this thesis asymmetries arise from various sources. They are observed in both conditional and unconditional distributions, for variables with non-negative values and for variables that have values on the real line. In the multivariate context asymmetries can be observed in the marginal distributions as well as in the relationships of the variables modeled. New methods for all these cases are proposed. Chapter 2 considers GARCH models and modeling of returns of two stock market indices. The chapter introduces the so-called generalized hyperbolic (GH) GARCH model to account for asymmetries in both conditional and unconditional distribution. In particular, two special cases of the GARCH-GH model which describe the data most accurately are proposed. They are found to improve the fit of the model when compared to symmetric GARCH models. The advantages of accounting for asymmetries are also observed through Value-at-Risk applications. Both theoretical and empirical contributions are provided in Chapter 3 of the thesis. In this chapter the so-called mixture conditional autoregressive range (MCARR) model is introduced, examined and applied to daily price ranges of the Hang Seng Index. The conditions for the strict and weak stationarity of the model as well as an expression for the autocorrelation function are obtained by writing the MCARR model as a first order autoregressive process with random coefficients. The chapter also introduces inverse gamma (IG) distribution to CARR models. The advantages of CARR-IG and MCARR-IG specifications over conventional CARR models are found in the empirical application both in- and out-of-sample. Chapter 4 discusses the simultaneous modeling of absolute returns and daily price ranges. In this part of the thesis a vector multiplicative error model (VMEM) with asymmetric Gumbel copula is found to provide substantial benefits over the existing VMEM models based on elliptical copulas. The proposed specification is able to capture the highly asymmetric dependence of the modeled variables thereby improving the performance of the model considerably. The economic significance of the results obtained is established when the information content of the volatility forecasts derived is examined.

Modeling of Convection and Macrosegregation through Appropriate Consideration of Multiphase/Multiscale Phenomena during Alloy Solidification

Solidification processes are complex in nature, involving multiple phases and several length scales. The properties of solidified products are dictated by the microstructure, the mactostructure, and various defects present in the casting. These, in turn, are governed by the multiphase transport phenomena Occurring at different length scales. In order to control and improve the quality of cast products, it is important to have a thorough understanding of various physical and physicochemical phenomena Occurring at various length scales. preferably through predictive models and controlled experiments. In this context, the modeling of transport phenomena during alloy solidification has evolved over the last few decades due to the complex multiscale nature of the problem. Despite this, a model accounting for all the important length scales directly is computationally prohibitive. Thus, in the past, single-phase continuum models have often been employed with respect to a single length scale to model solidification processing. However, continuous development in understanding the physics of solidification at various length scales oil one hand and the phenomenal growth of computational power oil the other have allowed researchers to use increasingly complex multiphase/multiscale models in recent. times. These models have allowed greater understanding of the coupled micro/macro nature of the process and have made it possible to predict solute segregation and microstructure evolution at different length scales. In this paper, a brief overview of the current status of modeling of convection and macrosegregation in alloy solidification processing is presented.

Theoretical modeling of ionospheric electrodynamics including induction effects

This thesis deals with theoretical modeling of the electrodynamics of auroral ionospheres. In the five research articles forming the main part of the thesis we have concentrated on two main themes: Development of new data-analysis techniques and study of inductive phenomena in the ionospheric electrodynamics. The introductory part of the thesis provides a background for these new results and places them in the wider context of ionospheric research. In this thesis we have developed a new tool (called 1D SECS) for analysing ground based magnetic measurements from a 1-dimensional magnetometer chain (usually aligned in the North-South direction) and a new method for obtaining ionospheric electric field from combined ground based magnetic measurements and estimated ionospheric electric conductance. Both these methods are based on earlier work, but contain important new features: 1D SECS respects the spherical geometry of large scale ionospheric electrojet systems and due to an innovative way of implementing boundary conditions the new method for obtaining electric fields can be applied also at local scale studies. These new calculation methods have been tested using both simulated and real data. The tests indicate that the new methods are more reliable than the previous techniques. Inductive phenomena are intimately related to temporal changes in electric currents. As the large scale ionospheric current systems change relatively slowly, in time scales of several minutes or hours, inductive effects are usually assumed to be negligible. However, during the past ten years, it has been realised that induction can play an important part in some ionospheric phenomena. In this thesis we have studied the role of inductive electric fields and currents in ionospheric electrodynamics. We have formulated the induction problem so that only ionospheric electric parameters are used in the calculations. This is in contrast to previous studies, which require knowledge of the magnetospheric-ionosphere coupling. We have applied our technique to several realistic models of typical auroral phenomena. The results indicate that inductive electric fields and currents are locally important during the most dynamical phenomena (like the westward travelling surge, WTS). In these situations induction may locally contribute up to 20-30% of the total ionospheric electric field and currents. Inductive phenomena do also change the field-aligned currents flowing between the ionosphere and magnetosphere, thus modifying the coupling between the two regions.

Numerical modeling of heat and mass transfer in laser surface alloying: Non-equilibrium solidification effects

A transient macroscopic model is developed for studying heat and mass transfer in a single-pass laser surface alloying process, with particular emphasis on non-equilibrium solidification considerations. The solution for species concentration distribution requires suitable treatment of non-equilibrium mass transfer conditions. In this context, microscopic features pertaining to non-equilibrium effects on account of solutal undercooling are incorporated through the formulation of a modified partition-coefficient. The effective partition-coefficient is numerically modeled by Means of a number of macroscopically observable parameters related to the solidifying domain. The numerical model is so developed that the modifications on account of non-equilibrium solidification considerations can be conveniently implemented in existing numerical codes based on equilibrium solidification considerations.

A vision modeling framework for DHM using geometrically estimated FoV

Digital human modeling (DHM) involves modeling of structure, form and functional capabilities of human users for ergonomics simulation. This paper presents application of geometric procedures for investigating the characteristics of human visual capabilities which are particularly important in the context mentioned above. Using the cone of unrestricted directions through the pupil on a tessellated head model as the geometric interpretation of the clinical field-of-view (FoV), the results obtained are experimentally validated. Estimating the pupil movement for a given gaze direction using Listing's Law, FoVs are re-computed. Significant variation of the FoV is observed with the variation in gaze direction. A novel cube-grid representation, which integrated the unit-cube representation of directions and the enhanced slice representation has been introduced for fast and exact point classification for point visibility analysis for a given FoV. Computation of containment frequency of every grid-cell for a given set of FoVs enabled determination of percentile-based FoV contours for estimating the visual performance of a given population. This is a new concept which makes visibility analysis more meaningful from ergonomics point-of-view. The algorithms are fast enough to support interactive analysis of reasonably complex scenes on a typical desktop computer. (C) 2011 Elsevier Ltd. All rights reserved.

Characterization and finite element modeling of montmorillonite/polypropylene nanocomposites

Finite element modeling can be a useful tool for predicting the behavior of composite materials and arriving at desirable filler contents for maximizing mechanical performance. In the present study, to corroborate finite element analysis results, quantitative information on the effect of reinforcing polypropylene (PP) with various proportions of nanoclay (in the range of 3-9% by weight) is obtained through experiments; in particular, attention is paid to the Young's modulus, tensile strength and failure strain. Micromechanical finite element analysis combined with Monte Carlo simulation have been carried out to establish the validity of the modeling procedure and accuracy of prediction by comparing against experimentally determined stiffness moduli of nanocomposites. In the same context, predictions of Young's modulus yielded by theoretical micromechanics-based models are compared with experimental results. Macromechanical modeling was done to capture the non-linear stress-strain behavior including failure observed in experiments as this is deemed to be a more viable tool for analyzing products made of nanocomposites including applications of dynamics. (C) 2011 Elsevier Ltd. All rights reserved.

Macromolecular Crystallography in India in the Global Context

The most spectacular applications of crystallography are currently concerned with biological macromolecules like proteins and their assemblies. Macromolecular crystallography originated in England in the thirties of the last century, but definitive results began to appear only around 1960. Since then macromolecular crystallography has grown to become central to modern biology. India has a long tradition in crystallography starting with the work of K. Banerjee in the thirties. In addition to their contributions to crystallography, G.N. Ramachandran and his colleagues gave a head start to India in computational biology, molecular modeling and what we now call bioinformatics. However, attempts to initiate macromolecular crystallography in India started only in the seventies. The work took off the ground after the Department of Science and Technology handsomely supported the group at Indian Institute of Science, Bangalore in 1983. The Bangalore group was also recognized as a national nucleus for the development of the area in the country. Since then macromolecular crystallography, practiced in more than 30 institutions in the country, has grown to become an important component of scientific research in India. The articles in this issue provide a flavor of activities in the area in the country. The area is still in an expanding phase and is poised to scale greater heights.

Accurate prediction of interfacial residues in two-domain proteins using evolutionary information: Implications for three-dimensional modeling

With the preponderance of multidomain proteins in eukaryotic genomes, it is essential to recognize the constituent domains and their functions. Often function involves communications across the domain interfaces, and the knowledge of the interacting sites is essential to our understanding of the structure-function relationship. Using evolutionary information extracted from homologous domains in at least two diverse domain architectures (single and multidomain), we predict the interface residues corresponding to domains from the two-domain proteins. We also use information from the three-dimensional structures of individual domains of two-domain proteins to train naive Bayes classifier model to predict the interfacial residues. Our predictions are highly accurate (approximate to 85%) and specific (approximate to 95%) to the domain-domain interfaces. This method is specific to multidomain proteins which contain domains in at least more than one protein architectural context. Using predicted residues to constrain domain-domain interaction, rigid-body docking was able to provide us with accurate full-length protein structures with correct orientation of domains. We believe that these results can be of considerable interest toward rational protein and interaction design, apart from providing us with valuable information on the nature of interactions. Proteins 2014; 82:1219-1234. (c) 2013 Wiley Periodicals, Inc.