AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Structure and representation of ecological data to support knowledge discovery: A case study with bioacoustic data

Bird communities have long been surveyed as key indicators of ecosystem health and biodiversity. Adoption of Autonomous Recording Units (ARUs) to perform avian surveys has shifted the burden of species recognition from “birders” in the field, to “listeners” who review the ARU recordings at a later time. The number of recordings ARUs can produce has created a need to process large amounts of data. Although much research is devoted to fully automating the recognition process, expert humans are still required when entire bird communities must be identified. A framework for a Decision Support System (DSS) is presented which would assist listeners by suggesting likely species. A unique feature of the DSS is the consideration of the recording “context” of time, location and habitat as well as the bioacoustic features to match unknown vocalizations with reference species. In this thesis a data warehouse was built for an existing set of bioacoustic research data as a first–step to creating the DSS. The data set was from ARU deployments in the Lower Athabasca Region of Alberta, Canada. The Knowledge Discovery in Databases (KDD) and Dimensional Design Process protocols were used as guides to build a Kimball–style data warehouse. Data housed in the data warehouse included field data, data derived from GIS analysis, fuzzy logic memberships and symbolic representation of bioacoustic recording using the Piecewise Aggregate Approximation and Symbolic Aggregate approXimation (PAA/SAX). Examples of how missing and erroneous data were detected and processed are given. The sources of uncertainty inherent in ecological data are discussed and fuzzy logic is demonstrated as a soft–computing technique to accommodate this data. Data warehouses are commonly used for business applications but are very applicable for ecological data. As most instructions on building data warehouse are for business data, this thesis is offered as an example for ecologists interested in moving their data to a data warehouse. This thesis presents a case–study of how a data warehouse can be constructed for existing ecological data, whether as part of a DSS or a tool for viewing research data.Symbolic aggregate approximationBioacousticsDecision support systemData warehouseFuzzy logicBirdsAutonomous recording unitsPiecewise aggregate approximatio

Structure and representation of ecological data to support knowledge discovery: A case study with bioacoustic data

Bird communities have long been surveyed as key indicators of ecosystem health and biodiversity. Adoption of Autonomous Recording Units (ARUs) to perform avian surveys has shifted the burden of species recognition from “birders” in the field, to “listeners” who review the ARU recordings at a later time. The number of recordings ARUs can produce has created a need to process large amounts of data. Although much research is devoted to fully automating the recognition process, expert humans are still required when entire bird communities must be identified. A framework for a Decision Support System (DSS) is presented which would assist listeners by suggesting likely species. A unique feature of the DSS is the consideration of the recording “context” of time, location and habitat as well as the bioacoustic features to match unknown vocalizations with reference species. In this thesis a data warehouse was built for an existing set of bioacoustic research data as a first–step to creating the DSS. The data set was from ARU deployments in the Lower Athabasca Region of Alberta, Canada. The Knowledge Discovery in Databases (KDD) and Dimensional Design Process protocols were used as guides to build a Kimball–style data warehouse. Data housed in the data warehouse included field data, data derived from GIS analysis, fuzzy logic memberships and symbolic representation of bioacoustic recording using the Piecewise Aggregate Approximation and Symbolic Aggregate approXimation (PAA/SAX). Examples of how missing and erroneous data were detected and processed are given. The sources of uncertainty inherent in ecological data are discussed and fuzzy logic is demonstrated as a soft–computing technique to accommodate this data. Data warehouses are commonly used for business applications but are very applicable for ecological data. As most instructions on building data warehouse are for business data, this thesis is offered as an example for ecologists interested in moving their data to a data warehouse. This thesis presents a case–study of how a data warehouse can be constructed for existing ecological data, whether as part of a DSS or a tool for viewing research data.Symbolic aggregate approximationBioacousticsDecision support systemData warehouseFuzzy logicBirdsAutonomous recording unitsPiecewise aggregate approximatio

Fisheries Associated with Mangrove Ecosystem in Indonesia: a View From a Mangrove Ecologist

Blessed with mangrove area of some 9.6 million ha in extent, Indonesia represents an important country with fishery resources being a source of food and nutrients. The fishery resources utilized by man, such as fishes, crustaceans and mollusks that are found in the mangrove ecosystem/swamp area arc enormous. There is a range of species caught in the mangrove and surrounding areas with over 70 species. However, commercially valued species are limited to a few such as rabbit fish, snapper, grouper, marline catfish, fringe-scale sardine, and anchovy. Leaf detritus from mangroves contribute a major energy input into fisheries. But information about the study on the relationship between fishery species and mangroves, ecologically and biologically, arc scanty. The mangrove is a physiographic unit, the principal components of which arc organisms. Therefore, the problems are predominantly of a biological nature (e.g., mangroves - fishery relationship). Positive correlation between the mangrove area and penaeid shrimp catch found in Indonesia, the Philippines, Australia and Mexico. Finally, the most important part of the variance of the MSY (Maximum Sustainable Yield) of penaieds (53% of the variance) could be explained by a combination of area of mangrove habitats and latitude. Keywords : Indonesia/Mangrove/Ecosystem/Fisheries/Ecology/Coastal areas/Fishes/Molluscans/ Crustaceans

Biodiversity Databases

Computing and database management has shifted from cottage industry-style methods — the small independent researcher keeping records for a particular project — to state-of-the-art file storage systems, presentation, and distribution over the Internet. New and emerging techniques for recognition, compilation, and data management have made managing data a discipline in its own right. Covering all aspects of this data management, Biodiversity Databases: Techniques, Politics, and Applications brings together input from social scientists, programmers, database designers, and information specialists to delineate the political setting and give institutions platforms for the dissemination of taxonomic information. A practical and logical guide to complex issues, the book explores the changes and challenges of the information age. It discusses projects developed to provide better access to all available biodiversity information. The chapters make the case for the need for representation of concepts in taxonomic databases. They explore issues involved in connecting databases with different user interfaces, the technical demands of linking databases that are not entirely uniform in structure, and the problems of user access and the control of data quality. The book highlights different approaches to addressing concerns associated with the taxonomic impediment and the low reproducibility of taxonomic data. It provides an in-depth examination of the challenge of making taxonomic information more widely available to users in the wider scientific community, in government, and the general population

FISHERIES ASSOCIATED WITH MANGROVE ECOSYSTEM IN INDONESIA: A View from a Mangrove Ecologist

Blessed with mangrove area of some 9.6 million ha in extent, Indonesia represents an important country with fishery resources being a source of food and nutrients. The fishery resources utilized by man, such as fishes, crustaceans and mollusks that are found in the mangrove ecosystem/swamp area arc enormous. There is a range of species caught in the mangrove and surrounding areas with over 70 species. However, commercially valued species are limited to a few such as rabbit fish, snapper, grouper, marline catfish, fringe-scale sardine, and anchovy. Leaf detritus from mangroves contribute a major energy input into fisheries. But information about the study on the relationship between fishery species and mangroves, ecologically and biologically, arc scanty. The mangrove is a physiographic unit, the principal components of which arc organisms. Therefore, the problems are predominantly of a biological nature (e.g., mangroves - fishery relationship). Positive correlation between the mangrove area and penaeid shrimp catch found in Indonesia, the Philippines, Australia and Mexico. Finally, the most important part of the variance of the MSY (Maximum Sustainable Yield) of penaieds (53% of the variance) could be explained by a combination of area of mangrove habitats and latitude. Keywords  :   Indonesia/Mangrove/Ecosystem/Fisheries/Ecology/Coastal areas/Fishes/Molluscans/ Crustaceans

Why reinvent the wheel: Let's build question answering systems together

Modern question answering (QA) systems need to flexibly integrate a number of components specialised to fulfil specific tasks in a QA pipeline. Key QA tasks include Named Entity Recognition and Disambiguation, Relation Extraction, and Query Building. Since a number of different software components exist that implement different strategies for each of these tasks, it is a major challenge to select and combine the most suitable components into a QA system, given the characteristics of a question. We study this optimisation problem and train classifiers, which take features of a question as input and have the goal of optimising the selection of QA components based on those features. We then devise a greedy algorithm to identify the pipelines that include the suitable components and can effectively answer the given question. We implement this model within Frankenstein, a QA framework able to select QA components and compose QA pipelines. We evaluate the effectiveness of the pipelines generated by Frankenstein using the QALD and LC-QuAD benchmarks. These results not only suggest that Frankenstein precisely solves the QA optimisation problem but also enables the automatic composition of optimised QA pipelines, which outperform the static Baseline QA pipeline. Thanks to this flexible and fully automated pipeline generation process, new QA components can be easily included in Frankenstein, thus improving the performance of the generated pipelines

Benchmarking environmental machine-learning models: methodological progress and an application to forest health

Geospatial machine learning is a versatile approach to analyze environmental data and can help to better understand the interactions and current state of our environment. Due to the artificial intelligence of these algorithms, complex relationships can possibly be discovered which might be missed by other analysis methods. Modeling the interaction of creatures with their environment is referred to as ecological modeling, which is a subcategory of environmental modeling. A subfield of ecological modeling is SDM, which aims to understand the relation between the presence or absence of certain species in their environments. SDM is different from classical mapping/detection analysis. While the latter primarily aim for a visual representation of a species spatial distribution, the former focuses on using the available data to build models and interpreting these. Because no single best option exists to build such models, different settings need to be evaluated and compared against each other. When conducting such modeling comparisons, which are commonly referred to as benchmarking, care needs to be taken throughout the analysis steps to achieve meaningful and unbiased results. These steps are composed out of data preprocessing, model optimization and performance assessment. While these general principles apply to any modeling analysis, their application in an environmental context often requires additional care with respect to data handling, possibly hidden underlying data effects and model selection. To conduct all in a programmatic (and efficient) way, toolboxes in the form of programming modules or packages are needed. This work makes methodological contributions which focus on efficient, machine-learning based analysis of environmental data. In addition, research software to generalize and simplify the described process has been created throughout this work