Benefits of the application of web-mining methods and techniques for the field of analytical customer relationship management of the marketing function in a knowledge management perspective

Le Web Mining (WM) reste une technologie relativement méconnue. Toutefois, si elle est utilisée adéquatement, elle s'avère être d'une grande utilité pour l'identification des profils et des comportements des clients prospects et existants, dans un contexte internet. Les avancées techniques du WM améliorent grandement le volet analytique de la Gestion de la Relation Client (GRC). Cette étude suit une approche exploratoire afin de déterminer si le WM atteint, à lui seul, tous les objectifs fondamentaux de la GRC, ou le cas échéant, devrait être utilisé de manière conjointe avec la recherche marketing traditionnelle et les méthodes classiques de la GRC analytique (GRCa) pour optimiser la GRC, et de fait le marketing, dans un contexte internet. La connaissance obtenue par le WM peut ensuite être administrée au sein de l'organisation dans un cadre de Gestion de la Connaissance (GC), afin d'optimiser les relations avec les clients nouveaux et/ou existants, améliorer leur expérience client et ultimement, leur fournir de la meilleure valeur. Dans un cadre de recherche exploratoire, des entrevues semi-structurés et en profondeur furent menées afin d'obtenir le point de vue de plusieurs experts en (web) data rnining. L'étude révéla que le WM est bien approprié pour segmenter les clients prospects et existants, pour comprendre les comportements transactionnels en ligne des clients existants et prospects, ainsi que pour déterminer le statut de loyauté (ou de défection) des clients existants. Il constitue, à ce titre, un outil d'une redoutable efficacité prédictive par le biais de la classification et de l'estimation, mais aussi descriptive par le biais de la segmentation et de l'association. En revanche, le WM est moins performant dans la compréhension des dimensions sous-jacentes, moins évidentes du comportement client. L'utilisation du WM est moins appropriée pour remplir des objectifs liés à la description de la manière dont les clients existants ou prospects développent loyauté, satisfaction, défection ou attachement envers une enseigne sur internet. Cet exercice est d'autant plus difficile que la communication multicanale dans laquelle évoluent les consommateurs a une forte influence sur les relations qu'ils développent avec une marque. Ainsi le comportement en ligne ne serait qu'une transposition ou tout du moins une extension du comportement du consommateur lorsqu'il n'est pas en ligne. Le WM est également un outil relativement incomplet pour identifier le développement de la défection vers et depuis les concurrents ainsi que le développement de la loyauté envers ces derniers. Le WM nécessite toujours d'être complété par la recherche marketing traditionnelle afin d'atteindre ces objectives plus difficiles mais essentiels de la GRCa. Finalement, les conclusions de cette recherche sont principalement dirigées à l'encontre des firmes et des gestionnaires plus que du côté des clients-internautes, car ces premiers plus que ces derniers possèdent les ressources et les processus pour mettre en œuvre les projets de recherche en WM décrits.\ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : Web mining, Gestion de la connaissance, Gestion de la relation client, Données internet, Comportement du consommateur, Forage de données, Connaissance du consommateu

On the Recognition of Emotion from Physiological Data

This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure

A fresh engineering approach for the forecast of financial index volatility and hedging strategies

This thesis attempts a new light on a problem of importance in Financial Engineering. Volatility is a commonly accepted measure of risk in the investment field. The daily volatility is the determining factor in evaluating option prices and in conducting different hedging strategies. The volatility estimation and forecast are still far from successfully complete for industry acceptance, judged by their generally lower than 50% forecasting accuracy. By judiciously coordinating the current engineering theory and analytical techniques such as wavelet transform, evolutionary algorithms in a Time Series Data Mining framework, and the Markov chain based discrete stochastic optimization methods, this work formulates a systematic strategy to characterize and forecast crucial as well as critical financial time series. Typical forecast features have been extracted from different index volatility data sets which exhibit abrupt drops, jumps and other embedded nonlinear characteristics so that accuracy of forecasting can be markedly improved in comparison with those of the currently prevalent methods adopted in the industry. The key aspect of the presented approach is "transformation and sequential deployment": i) transform the data from being non-observable to observable i.e., from variance into integrated volatility; ii) conduct the wavelet transform to determine the optimal forecasting horizon; iii) transform the wavelet coefficients into 4-lag recursive data sets or viewed differently as a Markov chain; iv) apply certain genetic algorithms to extract a group of rules that characterize different patterns embedded or hidden in the data and attempt to forecast the directions/ranges of the one-step ahead events; and v)apply genetic programming to forecast the values of the one-step ahead events. By following such a step by step approach, complicated problems of time series forecasting become less complex and readily resolvable for industry application. To implement such an approach, the one year, two year and five year S&PlOO historical data are used as training sets to derive a group of 100 rules that best describe their respective signal characteristics. These rules are then used to forecast the subsequent out-of-sample time series data. This set of tests produces an average of over 75% of correct forecasting rate that surpasses any other publicly available forecast results on any type of financial indices. Genetic programming was then applied on the out of sample data set to forecast the actual value of the one step-ahead event. The forecasting accuracy reaches an average of 70%, which is a marked improvement over other current forecasts. To validate the proposed approach, indices of S&P500 as well as S&P 100 data are tested with the discrete stochastic optimization method, which is based on Markov chain theory and involves genetic algorithms. Results are further validated by the bootstrapping operation. All these trials showed a good reliability of the proposed methodology in this research work. Finally, the thus established methodology has been shown to have broad applications in option pricing, hedging, risk management, VaR determination, etc

Ways and Capacity in Archaeological Data Management in Serbia

Over the past year and due to the COVID-19 pandemic, the entire world has witnessed inequalities across borders and societies. They also include access to archaeological resources, both physical and digital. Both archaeological data creators and users spent a lot of time working from their homes, away from artefact collections and research data. However, this was the perfect moment to understand the importance of making data freely and openly available, both nationally and internationally. This is why the authors of this paper chose to make a selection of data bases from various institutions responsible for preservation and protection of cultural heritage, in order to understand their policies regarding accessibility and usage of the data they keep. This will be done by simple visits to various web-sites or data bases. They intend to check on the volume and content, but also importance of the offered archaeological heritage. In addition, the authors will estimate whether the heritage has adequately been classified and described and also check whether data is available in foreign languages. It needs to be seen whether it is possible to access digital objects (documents and the accompanying metadata), whether access is opened for all users or it requires a certain hierarchy access, what is the policy of usage, reusage and distribution etc. It remains to be seen whether there are public API or whether it is possible to collect data through API. In case that there is a public API, one needs to check whether datasets are interoperable or messy, requiring data cleaning. After having visited a certain number of web-sites, the authors expect to collect enough data to make a satisfactory conclusion about accessibility and usage of Serbian archaeological data web bases

Neolithic land-use in the Dutch wetlands: estimating the land-use implications of resource exploitation strategies in the Middle Swifterbant Culture (4600-3900 BCE)

The Dutch wetlands witness the gradual adoption of Neolithic novelties by foraging societies during the Swifterbant period. Recent analyses provide new insights into the subsistence palette of Middle Swifterbant societies. Small-scale livestock herding and cultivation are in evidence at this time, but their importance if unclear. Within the framework of PAGES Land-use at 6000BP project, we aim to translate the information on resource exploitation into information on land-use that can be incorporated into global climate modelling efforts, with attention for the importance of agriculture. A reconstruction of patterns of resource exploitation and their land-use dimensions is complicated by methodological issues in comparing the results of varied recent investigations. Analyses of organic residues in ceramics have attested to the cooking of aquatic foods, ruminant meat, porcine meat, as well as rare cases of dairy. In terms of vegetative matter, some ceramics exclusively yielded evidence of wild plants, while others preserve cereal remains. Elevated δ15N values of human were interpreted as demonstrating an important aquatic component of the diet well into the 4th millennium BC. Yet recent assays on livestock remains suggest grazing on salt marshes partly accounts for the human values. Finally, renewed archaeozoological investigations have shown the early presence of domestic animals to be more limited than previously thought. We discuss the relative importance of exploited resources to produce a best-fit interpretation of changing patterns of land-use during the Middle Swifterbant phase. Our review combines recent archaeological data with wider data on anthropogenic influence on the landscape. Combining the results of plant macroremains, information from pollen cores about vegetation development, the structure of faunal assemblages, and finds of arable fields and dairy residue, we suggest the most parsimonious interpretation is one of a limited land-use footprint of cultivation and livestock keeping in Dutch wetlands between 4600 and 3900 BCE.NWOVidi 276-60-004Human Origin

Taphonomy, environment or human plant exploitation strategies?: Deciphering changes in Pleistocene-Holocene plant representation at Umhlatuzana rockshelter, South Africa

The period between ~40 and 20 ka BP encompassing the Middle Stone Age (MSA) and Later Stone Age (LSA) transition has long been of interest because of the associated technological change. Understanding this transition in southern Africa is complicated by the paucity of archaeological sites that span this period. With its occupation sequence spanning the last ~70,000 years, Umhlatuzana Rock Shelter is one of the few sites that record this transition. Umhlatuzana thus offers a great opportunity to study past environmental dynamics from the Late Pleistocene (MIS 4) to the Late Holocene, and past human subsistence strategies, their social organisation, technological and symbolic innovations. Although organic preservation is poor (bones, seeds, and charcoal) at the site, silica phytoliths preserve generally well throughout the sequence. These microscopic silica particles can identify different plant types that are no longer visible at the site because of decomposition or burning to a reliable taxonomical level. Thus, to trace site occupation, plant resource use, and in turn reconstruct past vegetation, we applied phytolith analyses to sediment samples of the newly excavated Umhlatuzana sequence. We present results of the phytolith assemblage variability to determine change in plant use from the Pleistocene to the Holocene and discuss them in relation to taphonomical processes and human plant gathering strategies and activities. This study ultimately seeks to provide a palaeoenvironmental context for modes of occupation and will shed light on past human-environmental interactions in eastern South Africa.NWOVidi 276-60-004Human Origin