A MapReduce architecture for web site user behaviour monitoring in real time

Monitoring the behaviour of large numbers of web site users in real time poses significant performance challenges, due to the decentralised location and volume of generated data. This paper proposes a MapReduce-style architecture where the processing of event series from the Web users is performed by a number of cascading mappers, reducers and rereducers, local to the event origin. With the use of static analysis and a prototype implementation, we show how this architecture is capable to carry out time series analysis in real time for very large web data sets, based on the actual events, instead of resorting to sampling or other extrapolation techniques

SVM categorizer: a generic categorization tool using support vector machines

Supervised text categorisation is a significant tool considering the vast amount of structured, unstruc-tured, or semi-structured texts that are available from internal or external enterprise resources. The goal of supervised text categorisation is to assign text documents to finite pre-specified categories in order to extract and automatically organise information coming from these resources. This paper pro-poses the implementation of a generic application – SVM Categorizer using the Support Vector Ma-chines algorithm with an innovative statistical adjustment that improves its performance. The algo-rithm is able to learn from a pre-categorised document corpus and it is tested on another uncatego-rized one based on a business intelligence case study. This paper discusses the requirements, design and implementation and describes every aspect of the application that will be developed. The final output of the SVM Categorizer is evaluated using commonly accepted metrics so as to measure its per-formance and contrast it with other classification tools

The Role of Big Data to Facilitate Redistributed Manufacturing Using a Co-creation Lens: Patterns from Consumer Goods

Manufacturing digitalisation and the growth of big data promises to foster more responsive supply chains and to close gaps between manufacturers and consumers, leading to highly-connected manufacturing operations, mass customisation and more sustainable production. There is widespread recognition that manufacturing in broad terms is entering a new period of transition and change, aided by new technologies and business models and with multiple predictions that there will be significant reconfigurations in the geographical and inclusive distribution of manufacturing operations. A concept that can be used to describe this process of transformation is called redistributed manufacturing. This concept encompasses the empowerment of consumer-inclusive co-creation. In this paper, we investigate whether and how big data can facilitate redistributed manufacturing in consumer goods industries. The research sheds light on how businesses are starting to redistribute their functions among various stakeholders including consumers and co-creating value. The paper proposes a conceptual framework to stimulate and organise thinking about emerging interrelationships between big data, co-creation and redistributed manufacturing, built upon an extensive literature review and qualitative analysis of 15 cases from the consumer goods industry using primary and secondary data. Through these cases, we analyse existing co-creation practices in consumer goods industries, and how they are evolving their manufacturing configurations, their underlying drivers, the role of big data applications, and their impacts on the redistribution of manufacturing. Our analysis finds that big data applications are supporting and prompting redistributed manufacturing approaches in these consumer goods industries.EPSR

The Role of Big Data to Facilitate Redistributed Manufacturing Using a Co-creation Lens: Patterns from Consumer Goods

Manufacturing digitalisation and the growth of big data promises to foster more responsive supply chains and to close gaps between manufacturers and consumers, leading to highly-connected manufacturing operations, mass customisation and more sustainable production. There is widespread recognition that manufacturing in broad terms is entering a new period of transition and change, aided by new technologies and business models and with multiple predictions that there will be significant reconfigurations in the geographical and inclusive distribution of manufacturing operations. A concept that can be used to describe this process of transformation is called redistributed manufacturing. This concept encompasses the empowerment of consumer-inclusive co-creation. In this paper, we investigate whether and how big data can facilitate redistributed manufacturing in consumer goods industries. The research sheds light on how businesses are starting to redistribute their functions among various stakeholders including consumers and co-creating value. The paper proposes a conceptual framework to stimulate and organise thinking about emerging interrelationships between big data, co-creation and redistributed manufacturing, built upon an extensive literature review and qualitative analysis of 15 cases from the consumer goods industry using primary and secondary data. Through these cases, we analyse existing co-creation practices in consumer goods industries, and how they are evolving their manufacturing configurations, their underlying drivers, the role of big data applications, and their impacts on the redistribution of manufacturing. Our analysis finds that big data applications are supporting and prompting redistributed manufacturing approaches in these consumer goods industries.EPSR

Redistributed Manufacturing and the Impact of Big Data: A Consumer Goods Perspective

Digitalisation and the growth of big data promise greater customisation as well as change in how manufacturing is distributed. Yet, challenges arise in applying these new approaches in consumer goods industries that often emphasise mass production and extended supply chains. We build a conceptual framework to explore whether big data combined with new manufacturing technologies can facilitate redistributed manufacturing. Through analysis of 24 consumer goods industry cases using primary and secondary data, we investigate evolving manufacturing configurations, their underlying drivers, the role of big data applications, and their impact on the redistribution of manufacturing. We find some applications of redistributed manufacturing concepts, although in other cases existing manufacturing configurations are leveraged for high volume consumer goods products through big data analytics and market segmentation. The analysis indicates that the framework put forward in the paper has broader value in organising thinking about emerging interrelationships between big data and manufacturing.The work was supported by the Engineering and Physical Sciences Research Council (EPSRC) and the Economic and Social Research Council (ESRC) through The Network in Consumer Goods, Big Data and Re-Distributed Manufacturing (RECODE) hosted at Cranfield University under grant number EP/M017567/1

Tailoring temporal description logics for reasoning over temporal conceptual models

Temporal data models have been used to describe how data can evolve in the context of temporal databases. Both the Extended Entity-Relationship (EER) model and the Unified Modelling Language (UML) have been temporally extended to design temporal databases. To automatically check quality properties of conceptual schemas various encoding to Description Logics (DLs) have been proposed in the literature. On the other hand, reasoning on temporally extended DLs turn out to be too complex for effective reasoning ranging from 2ExpTime up to undecidable languages. We propose here to temporalize the ‘light-weight’ DL-Lite logics obtaining nice computational results while still being able to represent various constraints of temporal conceptual models. In particular, we consider temporal extensions of DL-Lite^N_bool, which was shown to be adequate for capturing non-temporal conceptual models without relationship inclusion, and its fragment DL-Lite^N_core with most primitive concept inclusions, which are nevertheless enough to represent almost all types of atemporal constraints (apart from covering)