Exploring demand and production planning challenges in the food processing industry:a case study

This paper explores demand and production management challenges in the food processing industry. The goal is to identify the main production planning constraints and secondly to explore how each of these constraints affects company’s performance in terms of costs and customer service level. A single case study methodology was preferred since it enabled the collection of in-depth data. Findings suggest that product shelf life, carcass utilization and production lead time are the main constraints affecting supply chain efficiency and hence, a single planning approach is not appropriate when different products have different technological and processing characteristics.

An improved memory management scheme for large scale graph computing engine GraphChi

GraphChi is the first reported disk-based graph engine that can handle billion-scale graphs on a single PC efficiently. GraphChi is able to execute several advanced data mining, graph mining and machine learning algorithms on very large graphs. With the novel technique of parallel sliding windows (PSW) to load subgraph from disk to memory for vertices and edges updating, it can achieve data processing performance close to and even better than those of mainstream distributed graph engines. GraphChi mentioned that its memory is not effectively utilized with large dataset, which leads to suboptimal computation performances. In this paper we are motivated by the concepts of 'pin ' from TurboGraph and 'ghost' from GraphLab to propose a new memory utilization mode for GraphChi, which is called Part-in-memory mode, to improve the GraphChi algorithm performance. The main idea is to pin a fixed part of data inside the memory during the whole computing process. Part-in-memory mode is successfully implemented with only about 40 additional lines of code to the original GraphChi engine. Extensive experiments are performed with large real datasets (including Twitter graph with 1.4 billion edges). The preliminary results show that Part-in-memory mode memory management approach effectively reduces the GraphChi running time by up to 60% in PageRank algorithm. Interestingly it is found that a larger portion of data pinned in memory does not always lead to better performance in the case that the whole dataset cannot be fitted in memory. There exists an optimal portion of data which should be kept in the memory to achieve the best computational performance.

An Algorithmic Solution for Management of Related Text Objects with Application in Phytopharmacy

This paper presents an algorithmic solution for management of related text objects, in which are integrated algorithms for their extraction from paper or electronic format, for their storage and processing in a relational database. The developed algorithms for data extraction and data analysis enable one to find specific features and relations between the text objects from the database. The algorithmic solution is applied to data from the field of phytopharmacy in Bulgaria. It can be used as a tool and methodology for other subject areas where there are complex relationships between text objects.

A mathematical resolution to log transformations and the binning effect in applied processing of data in flow cytometry

This dissertation develops a new mathematical approach that overcomes the effect of a data processing phenomenon known as “histogram binning” inherent to flow cytometry data. A real-time procedure is introduced to prove the effectiveness and fast implementation of such an approach on real-world data. The histogram binning effect is a dilemma posed by two seemingly antagonistic developments: (1) flow cytometry data in its histogram form is extended in its dynamic range to improve its analysis and interpretation, and (2) the inevitable dynamic range extension introduces an unwelcome side effect, the binning effect, which skews the statistics of the data, undermining as a consequence the accuracy of the analysis and the eventual interpretation of the data. ^ Researchers in the field contended with such a dilemma for many years, resorting either to hardware approaches that are rather costly with inherent calibration and noise effects; or have developed software techniques based on filtering the binning effect but without successfully preserving the statistical content of the original data. ^ The mathematical approach introduced in this dissertation is so appealing that a patent application has been filed. The contribution of this dissertation is an incremental scientific innovation based on a mathematical framework that will allow researchers in the field of flow cytometry to improve the interpretation of data knowing that its statistical meaning has been faithfully preserved for its optimized analysis. Furthermore, with the same mathematical foundation, proof of the origin of such an inherent artifact is provided. ^ These results are unique in that new mathematical derivations are established to define and solve the critical problem of the binning effect faced at the experimental assessment level, providing a data platform that preserves its statistical content. ^ In addition, a novel method for accumulating the log-transformed data was developed. This new method uses the properties of the transformation of statistical distributions to accumulate the output histogram in a non-integer and multi-channel fashion. Although the mathematics of this new mapping technique seem intricate, the concise nature of the derivations allow for an implementation procedure that lends itself to a real-time implementation using lookup tables, a task that is also introduced in this dissertation. ^

Database As A Tool For Hospitality Management

In his discussion - Database As A Tool For Hospitality Management - William O'Brien, Assistant Professor, School of Hospitality Management at Florida International University, O’Brien offers at the outset, “Database systems offer sweeping possibilities for better management of information in the hospitality industry. The author discusses what such systems are capable of accomplishing.” The author opens with a bit of background on database system development, which also lends an impression as to the complexion of the rest of the article; uh, it’s a shade technical. “In early 1981, Ashton-Tate introduced dBase 11. It was the first microcomputer database management processor to offer relational capabilities and a user-friendly query system combined with a fast, convenient report writer,” O’Brien informs. “When 16-bit microcomputers such as the IBM PC series were introduced late the following year, more powerful database products followed: dBase 111, Friday!, and Framework. The effect on the entire business community, and the hospitality industry in particular, has been remarkable”, he further offers with his informed outlook. Professor O’Brien offers a few anecdotal situations to illustrate how much a comprehensive data-base system means to a hospitality operation, especially when billing is involved. Although attitudes about computer systems, as well as the systems themselves have changed since this article was written, there is pertinent, fundamental information to be gleaned. In regards to the digression of the personal touch when a customer is engaged with a computer system, O’Brien says, “A modern data processing system should not force an employee to treat valued customers as numbers…” He also cautions, “Any computer system that decreases the availability of the personal touch is simply unacceptable.” In a system’s ability to process information, O’Brien suggests that in the past businesses were so enamored with just having an automated system that they failed to take full advantage of its capabilities. O’Brien says that a lot of savings, in time and money, went un-noticed and/or under-appreciated. Today, everyone has an integrated system, and the wise business manager is the business manager who takes full advantage of all his resources. O’Brien invokes the 80/20 rule, and offers, “…the last 20 percent of results costs 80 percent of the effort. But times have changed. Everyone is automating data management, so that last 20 percent that could be ignored a short time ago represents a significant competitive differential.” The evolution of data systems takes center stage for much of the article; pitfalls also emerge.

Trajectories of Vegetation Response to Water Management in Taylor Slough, Everglades National Park, Florida

Ecosystem management practices that modify the major drivers and stressors of an ecosystem often lead to changes in plant community composition. This paper examines how closely the trajectory of vegetation change in seasonally-flooded wetlands tracks management-induced alterations in hydrology and soil characteristics. We used trajectory analysis, a multivariate method designed to test hypotheses about rates and directions of community change, to examine vegetation shifts in response to changes in water management practices within the Taylor Slough basin of Everglades National Park. We summarized vegetation data by non-metric multidimensional scaling ordination, and examined the time trajectory of each site along environmental vectors representing hydrology and soil phosphorus gradients. In the Taylor Slough basin, vegetation change trajectories closely followed the hydrologic changes caused by the operation of water pumps and detention ponds adjacent to the canals. We also observed a shift in vegetation composition along a vector of increasing soil phosphorus, which suggests the need for implementing measures to avoid P-enrichment in southern Everglades marl prairies. This study indicates that shifts in vegetation composition in response to changes in hydrologic conditions and associated parameters may be detected through trajectory analysis, thereby providing feedback for adaptive management of wetland ecosystems.

Developing a Data-Driven Classification of South Florida Plant Communities

A comprehensive, broadly accepted vegetation classification is important for ecosystem management, particularly for planning and monitoring. South Florida vegetation classification systems that are currently in use were largely arrived at subjectively and intuitively with the involvement of experienced botanical observers and ecologists, but with little support in terms of quantitative field data. The need to develop a field data-driven classification of South Florida vegetation that builds on the ecological organization has been recognized by the National Park Service and vegetation practitioners in the region. The present work, funded by the National Park Service Inventory and Monitoring Program - South Florida/Caribbean Network (SFCN), covers the first stage of a larger project whose goal is to apply extant vegetation data to test, and revise as necessary, an existing, widely used classification (Rutchey et al. 2006). The objectives of the first phase of the project were (1) to identify useful existing datasets, (2) to collect these data and compile them into a geodatabase, (3) to conduct an initial classification analysis of marsh sites, and (4) to design a strategy for augmenting existing information from poorly represented landscapes in order to develop a more comprehensive south Florida classification.

A mathematical resolution to log transformations and the binning effect in applied processing of data in flow cytometry

This dissertation develops a new mathematical approach that overcomes the effect of a data processing phenomenon known as "histogram binning" inherent to flow cytometry data. A real-time procedure is introduced to prove the effectiveness and fast implementation of such an approach on real-world data. The histogram binning effect is a dilemma posed by two seemingly antagonistic developments: (1) flow cytometry data in its histogram form is extended in its dynamic range to improve its analysis and interpretation, and (2) the inevitable dynamic range extension introduces an unwelcome side effect, the binning effect, which skews the statistics of the data, undermining as a consequence the accuracy of the analysis and the eventual interpretation of the data. Researchers in the field contended with such a dilemma for many years, resorting either to hardware approaches that are rather costly with inherent calibration and noise effects; or have developed software techniques based on filtering the binning effect but without successfully preserving the statistical content of the original data. The mathematical approach introduced in this dissertation is so appealing that a patent application has been filed. The contribution of this dissertation is an incremental scientific innovation based on a mathematical framework that will allow researchers in the field of flow cytometry to improve the interpretation of data knowing that its statistical meaning has been faithfully preserved for its optimized analysis. Furthermore, with the same mathematical foundation, proof of the origin of such an inherent artifact is provided. These results are unique in that new mathematical derivations are established to define and solve the critical problem of the binning effect faced at the experimental assessment level, providing a data platform that preserves its statistical content. In addition, a novel method for accumulating the log-transformed data was developed. This new method uses the properties of the transformation of statistical distributions to accumulate the output histogram in a non-integer and multi-channel fashion. Although the mathematics of this new mapping technique seem intricate, the concise nature of the derivations allow for an implementation procedure that lends itself to a real-time implementation using lookup tables, a task that is also introduced in this dissertation.

Inventory of SINOPS project data sets

During the SINOPS project, an optimal state of the art simulation of the marine silicon cycle is attempted employing a biogeochemical ocean general circulation model (BOGCM) through three particular time steps relevant for global (paleo-) climate. In order to tune the model optimally, results of the simulations are compared to a comprehensive data set of 'real' observations. SINOPS' scientific data management ensures that data structure becomes homogeneous throughout the project. Practical work routine comprises systematic progress from data acquisition, through preparation, processing, quality check and archiving, up to the presentation of data to the scientific community. Meta-information and analytical data are mapped by an n-dimensional catalogue in order to itemize the analytical value and to serve as an unambiguous identifier. In practice, data management is carried out by means of the online-accessible information system PANGAEA, which offers a tool set comprising a data warehouse, Graphical Information System (GIS), 2-D plot, cross-section plot, etc. and whose multidimensional data model promotes scientific data mining. Besides scientific and technical aspects, this alliance between scientific project team and data management crew serves to integrate the participants and allows them to gain mutual respect and appreciation.

End-to-end modelling for an ecosystem approach to fisheries in the Northern Humboldt Current Ecosystem

This work represents an original contribution to the methodology for ecosystem models' development as well as the rst attempt of an end-to-end (E2E) model of the Northern Humboldt Current Ecosystem (NHCE). The main purpose of the developed model is to build a tool for ecosystem-based management and decision making, reason why the credibility of the model is essential, and this can be assessed through confrontation to data. Additionally, the NHCE exhibits a high climatic and oceanographic variability at several scales, the major source of interannual variability being the interruption of the upwelling seasonality by the El Niño Southern Oscillation, which has direct e ects on larval survival and sh recruitment success. Fishing activity can also be highly variable, depending on the abundance and accessibility of the main shery resources. This context brings the two main methodological questions addressed in this thesis, through the development of an end-to-end model coupling the high trophic level model OSMOSE to the hydrodynamics and biogeochemical model ROMS-PISCES: i) how to calibrate ecosystem models using time series data and ii) how to incorporate the impact of the interannual variability of the environment and shing. First, this thesis highlights some issues related to the confrontation of complex ecosystem models to data and proposes a methodology for a sequential multi-phases calibration of ecosystem models. We propose two criteria to classify the parameters of a model: the model dependency and the time variability of the parameters. Then, these criteria along with the availability of approximate initial estimates are used as decision rules to determine which parameters need to be estimated, and their precedence order in the sequential calibration process. Additionally, a new Evolutionary Algorithm designed for the calibration of stochastic models (e.g Individual Based Model) and optimized for maximum likelihood estimation has been developed and applied to the calibration of the OSMOSE model to time series data. The environmental variability is explicit in the model: the ROMS-PISCES model forces the OSMOSE model and drives potential bottom-up e ects up the foodweb through plankton and sh trophic interactions, as well as through changes in the spatial distribution of sh. The latter e ect was taken into account using presence/ absence species distribution models which are traditionally assessed through a confusion matrix and the statistical metrics associated to it. However, when considering the prediction of the habitat against time, the variability in the spatial distribution of the habitat can be summarized and validated using the emerging patterns from the shape of the spatial distributions. We modeled the potential habitat of the main species of the Humboldt Current Ecosystem using several sources of information ( sheries, scienti c surveys and satellite monitoring of vessels) jointly with environmental data from remote sensing and in situ observations, from 1992 to 2008. The potential habitat was predicted over the study period with monthly resolution, and the model was validated using quantitative and qualitative information of the system using a pattern oriented approach. The nal ROMS-PISCES-OSMOSE E2E ecosystem model for the NHCE was calibrated using our evolutionary algorithm and a likelihood approach to t monthly time series data of landings, abundance indices and catch at length distributions from 1992 to 2008. To conclude, some potential applications of the model for shery management are presented and their limitations and perspectives discussed.

High-performance parallel systems for data-intensive computing

Thesis (Ph.D.)--University of Washington, 2016-08

A MODEL FOR EVALUATING IMPACT OF BUSINESS PROCESS MANAGEMENT IN ORGANIZATIONAL PERFORMANCE

Business Process Management (BPM) is able to organize and frame a company focusing in the improvement or assurance of performance in order to gain competitive advantage. Although it is believed that BPM improves various aspects of organizational performance, there has been a lack of empirical evidence about this. The present study has the purpose to develop a model to show the impact of business process management in organizational performance. To accomplish that, the theoretical basis required to know the elements that configurate BPM and the measures that can evaluate the BPM success on organizational performance is built through a systematic literature review (SLR). Then, a research model is proposed according to SLR results. Empirical data will be collected from a survey of  larg and mid-sized industrial and service companies headquartered in Brazil. A quantitative analysis will be performed using structural equation modeling (SEM) to show if the direct effects among BPM and organizational performance can be considered statistically significant. At the end will discuss these results and their managerial and cientific implications.Keywords: Business process management (BPM). Organizational performance. Firm performance. Business models. Structural Equation Modeling. Systematic Literature Review.