Piloting an Empirical Study on Measures for Workflow Similarity

Service discovery of state dependent services has to take workflow aspects into account. To increase the usability of a service discovery, the result list of services should be ordered with regard to the relevance of the services. Means of ordering a list of workflows due to their similarity with regard to a query are missing. This paper presents a pilot of an empirical study on the influence of different measures on workflow similarity. It turns out that, although preliminary, relations between different measures are indicated and that a similarity definition depends on the application scenario in which the service discovery is applied

Comparison of Simple Graphical Process Models

Comparing the structure of graphical process models can reveal a number of process variations. Since most contemporary norms for process modelling rely on directed connectivity of objects in the model, connections between objects form sequences which can be translated into performing scenarios. Whereas sequences can be tested for completeness in performing process activities using simulation methods, the similarity or difference in static characteristics of sequences in different model variants are difficult to explore. The goal of the paper is to test the application of a method for comparison of graphical models by analyzing and comparing static characteristics of process models. Consequently, a metamodel for process models is developed followed by a comparison procedure conducted using a graphical model comparison algorithm

Forecasting bus passenger flows by using a clustering-based support vector regression approach

As a significant component of the intelligent transportation system, forecasting bus passenger flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to varied destinations and departure times. For this reason, a novel forecasting model named as affinity propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally, the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate that the proposed model performs better than other peer models in terms of absolute percentage error and mean absolute percentage error. It is recommended that the deterministic clustering technique with stable cluster results (AP) can improve the forecasting performance significantly.info:eu-repo/semantics/publishedVersio

The Role of Geographic Proximity And Industrial Structure In Metropolitan Area Business Cycles

Measurement and prediction of aggregate economic fluctuations at the region, state, and metropolitan area level is a major challenge. As data quality and analytical techniques have improved, the analysis of coincident economic cycle indicators (CEI) has progressed from national to regional to state levels. This paper continues the trend of geographic disaggregation by constructing and analyzing CEI at the MSA level. The theoretical advantage of MSA level indexes is that they reflect labor market areas. Given lack of quarterly economic time series at the MSA level, we construct a new variable, the EPI (export price index). The EPI is an index number constructed to measure changes in the prices of goods produced by major industries located in metropolitan areas. Using non-agricultural employment and the EPI as MSA-specific variables, we are able to estimate following a Stock/Watson type single factor models. We find that, for larger states, with multiple MSAs, there is substantial variation in the amplitude and timing of cycles across MSAs. Further tests group MSAs within states by applying cluster analysis to the state series for the MSAs within a state. The groupings are interesting for two reasons. First, they confirm the differences observed within states. Secondly, and perhaps most important, the groupings of cyclically similar MSAs are not always based on geographic proximity as might be expected. It appears that industrial similarity of the MSA economies is also important for cyclical performance

Automated Measurement of Adherence to Traumatic Brain Injury (TBI) Guidelines using Neurological ICU Data

Using a combination of physiological and treatment information from neurological ICU data-sets, adherence to traumatic brain injury (TBI) guidelines on hypotension, intracranial pressure (ICP) and cerebral perfusion pressure (CPP) is calculated automatically. The ICU output is evaluated to capture pressure events and actions taken by clinical staff for patient management, and are then re-expressed as simplified process models. The official TBI guidelines from the Brain Trauma Foundation are similarly evaluated, so the two structures can be compared and a quantifiable distance between the two calculated (the measure of adherence). The methods used include: the compilation of physiological and treatment information into event logs and subsequently process models; the expression of the BTF guidelines in process models within the real-time context of the ICU; a calculation of distance between the two processes using two algorithms (“Direct” and “Weighted”) building on work conducted in th e business process domain. Results are presented across two categories each with clinical utility (minute-by-minute and single patient stays) using a real ICU data-set. Results of two sample patients using a weighted algorithm show a non-adherence level of 6.25% for 42 mins and 56.25% for 708 mins and non-adherence of 18.75% for 17 minutes and 56.25% for 483 minutes. Expressed as two combinatorial metrics (duration/non-adherence (A) and duration * non-adherence (B)), which together indicate the clinical importance of the non-adherence, one has a mean of A=4.63 and B=10014.16 and the other a mean of A=0.43 and B=500.0

Business Process Retrieval Based on Behavioral Semantics

This paper develops a framework for retrieving business processes considering search requirements based on behavioral semantics properties; it presents a framework called "BeMantics" for retrieving business processes based on structural, linguistics, and behavioral semantics properties. The relevance of the framework is evaluated retrieving business processes from a repository, and collecting a set of relevant business processes manually issued by human judges. The "BeMantics" framework scored high precision values (0.717) but low recall values (0.558), which implies that even when the framework avoided false negatives, it prone to false positives. The highest pre- cision value was scored in the linguistic criterion showing that using semantic inference in the tasks comparison allowed to reduce around 23.6 % the number of false positives. Using semantic inference to compare tasks of business processes can improve the precision; but if the ontologies are from narrow and specific domains, they limit the semantic expressiveness obtained with ontologies from more general domains. Regarding the perform- ance, it can be improved by using a filter phase which indexes business processes taking into account behavioral semantics propertie