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.