PERFORMANCE EVALUATION OF RESOURCE-AWARE BUSINESS PROCESSES USING STOCHASTIC AUTOMATA NETWORKS

In this work, we study the performance evaluation of resource-aware business process models. We define a new framework that allows the generation of analytical models for performance evaluation from business process models annotated with resource management information. This framework is composed of a new notation that allows the specification of resource management constraints and a method to convert a business process specification and its resource constraints into Stochastic Automata Networks (SANs). We show that the analysis of the generated SAN model provides several performance indices, such as average throughput of the system, average waiting time, average queues size, and utilization rate of resources. Using the BP2SAN tool - our implementation of the proposed framework - and a SAN solver (such as the PEPS tool) we show through a simple use-case how a business specialist with no skills in stochastic modeling can easily obtain performance indices that, in turn, can help to identify bottlenecks on the model, to perform workload characterization, to define the provisioning of resources, and to study other performance related aspects of the business process.

The SB-index and the HSB-index: efficient indices for spatial data warehouses

Spatial data warehouses (SDWs) allow for spatial analysis together with analytical multidimensional queries over huge volumes of data. The challenge is to retrieve data related to ad hoc spatial query windows according to spatial predicates, avoiding the high cost of joining large tables. Therefore, mechanisms to provide efficient query processing over SDWs are essential. In this paper, we propose two efficient indices for SDW: the SB-index and the HSB-index. The proposed indices share the following characteristics. They enable multidimensional queries with spatial predicate for SDW and also support predefined spatial hierarchies. Furthermore, they compute the spatial predicate and transform it into a conventional one, which can be evaluated together with other conventional predicates by accessing a star-join Bitmap index. While the SB-index has a sequential data structure, the HSB-index uses a hierarchical data structure to enable spatial objects clustering and a specialized buffer-pool to decrease the number of disk accesses. The advantages of the SB-index and the HSB-index over the DBMS resources for SDW indexing (i.e. star-join computation and materialized views) were investigated through performance tests, which issued roll-up operations extended with containment and intersection range queries. The performance results showed that improvements ranged from 68% up to 99% over both the star-join computation and the materialized view. Furthermore, the proposed indices proved to be very compact, adding only less than 1% to the storage requirements. Therefore, both the SB-index and the HSB-index are excellent choices for SDW indexing. Choosing between the SB-index and the HSB-index mainly depends on the query selectivity of spatial predicates. While low query selectivity benefits the HSB-index, the SB-index provides better performance for higher query selectivity.

Intelligence systems methodology: a systemic approach to the organizational intelligence function

Organizational intelligence can be seen as a function of the viable structure of an organization. With the integration of the Viable System Model and Soft Systems Methodology (systemic approaches of organizational management) focused on the role of the intelligence function, it is possible to elaborate a model of action with a structured methodology to prospect, select, treat and distribute information to the entire organization that improves the efficacy and efficiency of all processes. This combination of methodologies is called Intelligence Systems Methodology (ISM) whose assumptions and dynamics are delimited in this paper. The ISM is composed of two simultaneous activities: the Active Environmental Mapping and the Stimulated Action Cycle. The elaboration of the formal ISM description opens opportunities for applications of the methodology on real situations, offering a new path for this specific issue of systems thinking: the intelligence systems. Knowledge Management Research & Practice (2012) 10, 141-152. doi:10.1057/kmrp.2011.44

Toward the Optimization of an e-Tongue System Using Information Visualization: A Case Study with Perylene Tetracarboxylic Derivative Films in the Sensing Units

The wide variety of molecular architectures used in sensors and biosensors and the large amount of data generated with some principles of detection have motivated the use of computational methods, such as information visualization techniques, not only to handle the data but also to optimize sensing performance. In this study, we combine projection techniques with micro-Raman scattering and atomic force microscopy (AFM) to address critical issues related to practical applications of electronic tongues (e-tongues) based on impedance spectroscopy. Experimentally, we used sensing units made with thin films of a perylene derivative (AzoPTCD acronym), coating Pt interdigitated electrodes, to detect CuCl(2) (Cu(2+)), methylene blue (MB), and saccharose in aqueous solutions, which were selected due to their distinct molecular sizes and ionic character in solution. The AzoPTCD films were deposited from monolayers to 120 nm via Langmuir-Blodgett (LB) and physical vapor deposition (PVD) techniques. Because the main aspects investigated were how the interdigitated electrodes are coated by thin films (architecture on e-tongue) and the film thickness, we decided to employ the same material for all sensing units. The capacitance data were projected into a 2D plot using the force scheme method, from which we could infer that at low analyte concentrations the electrical response of the units was determined by the film thickness. Concentrations at 10 mu M or higher could be distinguished with thinner films tens of nanometers at most-which could withstand the impedance measurements, and without causing significant changes in the Raman signal for the AzoPTCD film-forming molecules. The sensitivity to the analytes appears to be related to adsorption on the film surface, as inferred from Raman spectroscopy data using MB as analyte and from the multidimensional projections. The analysis of the results presented may serve as a new route to select materials and molecular architectures for novel sensors and biosensors, in addition to suggesting ways to unravel the mechanisms behind the high sensitivity obtained in various sensors.

Firm Market Performance and Volatility in a National Real Estate Sector

We present empirical evidence using daily data for stock prices for 17 real estate companies traded in the Sao Paulo, Brazil stock exchange. from August 26, 2006 to March 31, 2010. We use the U.S. house price bubble, financial crisis and risk measures to instrument for momentums and reversals in the domestic real estate sector. We find evidence of conditional premium persistence and conditional volatility persistence in the market. We find that the conditional risk-return relationship in the sector is consistent with the prospect theory of risk attitudes in this period. Certain companies seem to be operating on a perceived potential industry return above the target, while most others are below the target, and the whole sector is below target on average. (C) 2011 Elsevier Inc. All rights reserved.

Normative Data for Healthy Middle-Aged and Elderly Performance on the Addenbrooke Cognitive Examination-Revised

Objective: To provide normative data for healthy middle-aged and elderly Brazilians' performance on the Addenbrooke Cognitive Examination-Revised (ACE-R) and to investigate the effects of age, sex, and schooling on test performance. Background: The ACE-R is a brief cognitive battery that assesses various aspects of cognition. Its 5 subdomains (Attention and Orientation, Memory, Verbal Fluency, Language, and Visuospatial Abilities) are commonly impaired in Alzheimer disease or frontotemporal dementia. Methods: We evaluated 144 cognitively healthy volunteers (50% men, 50% women) aged 50 to 93 years, with 4 to 24 years of schooling. We divided the participants into 4 age groups, each of which was then stratified into 3 groups according to years of education. We assessed all participants with the ACE-R, the Mattis Dementia Rating Scale, and the Cornell Scale for Depression in Dementia. Results: Years of education affected all ACE-R subscores. Age influenced the Verbal Fluency subscore (P < 0.001) and the ACE-R total score (P < 0.05). Sex affected the Attention and Orientation (P = 0.037) and Mini-Mental State Examination subscores (P = 0.048), but not the ACE-R total score (P > 0.05). Conclusions: The performance of healthy middle-aged and elderly individuals on the ACE-R battery is strongly influenced by education and, to a lesser extent, by age. These findings are of special relevance in countries with populations that have marked heterogeneity in educational levels.

Frailty criteria and cognitive performance are related: data from the fibra study in Ermelino Matarazzo, São Paulo, Brazil

Objectives: To assess the relationship between the CHS frailty criteria (Fried et al., 2001) and cognitive performance. Design: Cross sectional and population-based. Setting: Ermelino Matarazzo, a poor sub district of the city of Sao Paulo, Brazil. Participants: 384 community dwelling older adults, 65 and older. Measurements: Assessment of the CHS frailty criteria, the Brief Cognitive Screening Battery (memorization of 10 black and white pictures, verbal fluency animal category, and the Clock Drawing Test) and the Mini-Mental State Examination (MMSE). Results: Frail older adults performed significantly lower than non-frail and pre frail elderly in most cognitive variables. Grip strength and age were associated to MMSE performance, age was associated to delayed memory recall, gait speed was associated to verbal fluency and CDT performance, and education was associated to CDT performance. Conclusion: Being frail may be associated with cognitive decline, thus, gerontological assessments and interventions should consider that these forms of vulnerability may occur simultaneously.

Network-based high level data classification

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

Knowledge management and strategic orientation: leveraging innovativeness and performance

Purpose - The aim of this study is to investigate whether knowledge management (KM) contributes to the development of strategic orientation and to enhance innovativeness, and whether these three factors contribute to improve business performance. Design/methodology/approach - A sample of 241 Brazilian companies was surveyed, using Web-based questionnaires with 54 questions, using ten-point scales to measure the degree of agreement on each item of each construct. Structural equation modeling techniques were applied for model assessment and analysis of the relationships among constructs. Exploratory factor analysis, confirmatory factor analysis, and path analysis using the technique of structural equation modeling were applied to the data. Findings - Effective KM contributes positively to strategic orientation. Although there is no significant direct effect of KM on innovativeness, the relationship is significant when mediated by strategic orientation. Similarly effective KM has no direct effect on business performance, but this relationship becomes statistically significant when mediated by strategic orientation and innovativeness. Research limitations/implications - The findings indicate that KM permeates all relationships among the constructs, corroborating the argument that knowledge is an essential organizational resource that leverages all value-creating activities. The results indicate that both KM and innovativeness produce significant impacts on performance when they are aligned with a strategic orientation that enables the organization to anticipate and respond to changing market conditions. Originality/value - There is a substantial body of research on several types of relationships involving KM, strategic orientation, innovativeness and performance. This study offers an original contribution by analyzing all of those constructs simultaneously using established scales so that comparative studies are possible.

Organization Performance Evaluation Using System Thinking: A Study in Brazilian Chemical Organizations Models

System thinking allows companies to use subjective constructs indicators like recursiveness, cause-effect relationships and autonomy to performance evaluation. Thus, the question that motivates this paper is: Are Brazilian companies searching new performance measurement and evaluation models based on system thinking? The study investigates models looking for system thinking roots in their framework. It was both exploratory and descriptive based on a multiple four case studies strategy in chemical sector. The findings showed organizational models have some characteristics that can be related to system thinking as system control and communication. Complexity and autonomy are deficiently formalized by the companies. All data suggest, inside its context, that system thinking seems to be adequate to organizational performance evaluation but remains distant from the management proceedings.

On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data

Statistical methods have been widely employed to assess the capabilities of credit scoring classification models in order to reduce the risk of wrong decisions when granting credit facilities to clients. The predictive quality of a classification model can be evaluated based on measures such as sensitivity, specificity, predictive values, accuracy, correlation coefficients and information theoretical measures, such as relative entropy and mutual information. In this paper we analyze the performance of a naive logistic regression model (Hosmer & Lemeshow, 1989) and a logistic regression with state-dependent sample selection model (Cramer, 2004) applied to simulated data. Also, as a case study, the methodology is illustrated on a data set extracted from a Brazilian bank portfolio. Our simulation results so far revealed that there is no statistically significant difference in terms of predictive capacity between the naive logistic regression models and the logistic regression with state-dependent sample selection models. However, there is strong difference between the distributions of the estimated default probabilities from these two statistical modeling techniques, with the naive logistic regression models always underestimating such probabilities, particularly in the presence of balanced samples. (C) 2012 Elsevier Ltd. All rights reserved.

Synthesis and photovoltaic performance of a fluorene-bithiophene copolymer

We present the synthesis of a copolymer structure, poly(9,9′-n-di-hexyl-2,7-fluorene-alt-2,5- bithiophene), referred to herein as LaPPS43, and its physico-chemical characterization. Thin films of this polymer mixed with phenyl-C61-butyric acid methyl ester (PCBM) were used as the active layer in photovoltaic devices using the ITO/PEDOT:PSS/LaPPS43: PCBM/Ca/Al bulk heterojunction structure. The devices of different active layer thicknesses were electrically studied using J-V curves and the Photo-Celiv technique. The obtained results show that LaPPS43 combined with PCBM is a promising system for photovoltaic devices. Device performance is discussed in terms of the mean drift distance x for charge carriers. Photophysical data showed that the excitonic species are all localized in the aggregated forms. The mechanism of exciton formation and dissociation is also discussed.