There is a growing demand in the business-to-business environment for solutions that cater for specific industry needs as opposed to those for generic purposes. In response, technology vendors have gradually shifted their focus to industry solutions. More often than not, however, industry solutions are highly technical and complex, making it difficult for vendors to articulate the business impact and, as such, for customers to justify the investment. Although existing work provides a set of relevant building blocks for value modeling, they are yet to form an integrated approach. In recognizing the gap, this paper proposes Service Value Modeling (SVM), a systematic, step-wise method to connect industry solutions with business outcomes through the use of customer-specific business architecture. The SVM method is illustrated by two practical examples to show how exactly an industry solution affects the customer's operations and makes a positive impact on its performance. It was found that, with SVM, technology vendors are more likely to gain competitive advantage in both sales and delivery stages.
We present a case management approach to determining the upper and lower bounds on the price at which complex IT service contracts can be won or lost. Our approach is based on mining 'similar' prior deals and market benchmark data modeled as cases, to determine the upper and lower bounds on unit costs and unit prices for each of the service involved in an IT service solution such that the win probability of the deal is maximized. While there is no such a thing as an exact match in complex IT service solutions, as no two deals are alike, our approach offers a practical method for assessing the competitiveness of IT service solutions from unit cost and unit price perspective by leveraging prior won and lost deals. Experiments done on about 177 prior IT strategic outsourcing deals of a large IT service provider organization have resulted in a precision of 72% in determining the price at which one must not lose a deal under ceteris paribus assumption. Another main outcome of our study is the reconfirmation of the belief that price is a weak indicator of win or loss outcome in complex IT services where multiple factors are at play in clients' decision to award contracts.
This paper proposes granularity-based temporal data mining method which constructs clinical process conducted by nurses. The methods consist of three process. First, data on counting sum of executed orders are extracted from hospital information system with a given temporal granularity. Then, similarity-based methods, such as clustering and multidimensional scaling (MDS) are applied to the data and the labels for grouping are obtained. By using the labels, rule induction is applied, and classification power of each attribute is estimated. The attributes are sorted by an index of classification power, the original dataset is decomposed into sub tables. Clustering, rule induction and table decomposition methods are applied to the sub tables in a recursive way. The method was applied to datasets stored in hospital information system stored in 10 years. The results show that the reuse of stored data will give a powerful tool for construction of clinical process, which can be viewed as data-oriented management of nursing schedule.
Social media has been valuable sources to predict the future outcomes of some events such as box-office movie revenues or political elections. This paper focuses on periodic forecasting problems of product sales based on social media analysis and time-series analysis. In particular, we present a predictive model of monthly automobile sales using sentiment and topical keyword frequencies related to the target brand over time on social media. Our predictive model illustrates how different time scale-based predictors derived from sentiment and topical keyword frequencies can improve the prediction of the future sales.
Agri-Food Supply Chain Networks are required to increase production and to be transparent while reducing environmental impact. This challenges farm enterprises to innovate their production processes. These processes need to be supported by advanced ICT components that are developed by multiple vendors and owned by different enterprises. These ICT components should use future internet technologies to enable integration. To make these future internet technologies easily available, the European Commission started a Future Internet Public-Private Partnership Programme (FI-PPP). As part of this FI-PPP the FIspace project started with the development of a cloud-based platform to support business to business collaboration, enabling the formation of domain specific Software Ecosystems. These FIspace Software Ecosystems should drive the development of integrated and extensible collaboration services together with an initial set of domain applications that can support business processes and enables collaboration in networks. The FIspace Platform will enable integration of legacy systems, extending their functionality. This paper presents two use case scenarios for the smart application of pesticides to protect crops and keep them safe for diseases. These scenarios provide architectural descriptions how FIspace Platform configurations can support these processes. Based on these specific use case scenarios, generic use case scenarios and architectural descriptions are derived. These results can be used to develop FIspace Platform configurations supporting collaboration processes in other domains.
It is expected that the world population will further expand from the current 6 billion to 9-11 billion people in 2050. This faces us with an enormous challenge to feed this population and still keep production within the limits of planet Earth's carrying capacity. Smart Farming - i.e. the use of smart, data-rich ICT-services and applications, in combination with advanced hardware (in tractors, greenhouses, etc.) - can provide the much needed breakthroughs to producing enough good quality food in a safe and environmental-sound way. This paper introduces a Future Internet platform - called FIspace - for business to business collaboration that is currently being developed within Europe's Future Internet Public-Private Partnership programme (FI-PPP). On top of that a specific implementation will be made for the area of Smart Farming that will enable a global approach for Apps and Services development. It is expected that this will overcome many of the current bottlenecks in ICT development for Smart Farming such as interoperability and handling large amounts of data, and that it will lead to more agile and affordable software solutions. In this way, this development can contribute to the global challenge of producing enough safe and healthy food for the future within planet Earth's carrying capacity. New projects are planned and collaboration in Europe and beyond should further leverage the platform. The ambition is to become world-leading in this area.
Data or information lifecycle management (ILM) is the process of managing data over its lifecycle in a manner that balances cost and performance. The task is made difficult by data's continuously changing business value. If done well, it can lower costs through the increased use of cost-effective storage but also runs the risk of negatively impacting performance if data is inadvertently placed on the wrong device (e.g., low performance storage or on an over-utilized storage device). To address this challenge, we designed and developed the Intelligent Storage Tiering Manager (ISTM), an analytics-driven storage tiering tool that automates the process of load balancing data across and within different storage tiers in virtualized storage environments. Using administrator-generated policies, ISTM finds data with the specified performance profiles and automatically moves them to the appropriate storage tier. Application impact is minimized by limiting overall migration load and keeping data accessible during migration. Automation results in significantly less labor and errors while reducing task completion time from several days (and in some cases weeks) to a few hours. In this paper, we provide an overview of information lifecycle management (ILM), discuss existing solutions, and finally focus on the design and deployment of our ILM solution, ISTM, within a production data center.
With one of the fastest-aging populations in the world, Taiwan faces significant cultural and healthcare cost challenges. Taiwan has identified health care and aging as top priority issues for public health and welfare. Taipei, the prosperous capital, has devoted itself since 2004 to promoting health policy in keeping with World Health Organization (WHO) Healthy City trends and the mottos \"Health for All\" in the 21st century and, more recently, \"Active Aging.\" In this report, we present a system for managing healthcare information in Taipei called \"Citizen Telecare Service System\" (CTCS), which primarily targets active elders, low-income and remote area citizens. The system integrates several important features of hardware and software infrastructure, including biometric measurement, hypertension risk assessment, clinic appointment service, video communication, medical referral assistance, citizen health record, and health/hygiene education programs. The evaluation focused on demand and acceptability of the system in different population groups and across different settings like home, day care centers, and nursing homes. In-depth analysis of enrolled participants showed a reduction in the percentage of participants with systolic Blood pressure (SBP) above 140mmHg from 36.38% to 27.24%, and a drop in the prevalence of SBP above 120mmHg from 75.05% to 71.24%. The learned experiences and preliminary results could be a valuable lesson to share with other cities around the world about public telecare services for citizens.
Proper technology transfer based on scientific data is inevitable for farmers to promote sustainable agriculture which is a common target of modern food production. In under-developing countries, illiteracy of farmers is one of the major reasons for them to receive sufficient information and knowledge for such sustainable food production. In this study, we conducted a trial to examine effectiveness of an idea to transfer agricultural knowledge and information to illiterate farmers through their children educated at school as messengers between their illiterate parents and remote experts by utilizing several ICT tools including a multilingual machine translator which can bridge the famers and even foreign experts.
Departments of many organizations treat the World Wide Web as an important information source. They have a need to keep themselves up-to-date with current information in their domain. Such information gathering is a time consuming process due to overload of available information and there are dedicated teams in many organizations for this task. In this paper, we present Info Suggest, a system for end-to-end information gathering from the web. Info Suggest improves efficiency of such focused information gathering process with the use of machine learning. We employ a semi-supervised document classification method called Transductive Support Vector Machines (TSVMs) for learning user preferences based on example articles provided by them. We also devise a strategy for unlabeled data selection TSVM-Meta that is applicable for an information gathering setting. In the paper, we discuss the system architecture and also present a case study for information gathering for food safety in an environmental health department of a government agency. We conduct experiments and demonstrate that our system results in improving the efficiency by as much as 35% by making it easier to find relevant content.
Internet of things (IoT) is connected world of capabilities and services that enable interaction among physical objects supported by technologies such as sensors, RFID, wireless, mobile, cloud and internet. In this paper we analyze the drivers for IT services to use IoT and challenges involved and establish the need for accelerators. Various service applications can be built using IoT in all the verticals to improve process efficiency and productivity. We propose IoT Application Accelerator in this paper which is a light-weight IoT platform that can be used to rapidly prototype, develop and deploy value-added IoT applications, solutions and services. This paper highlights the case studies developed using the application accelerator platform and highlights the technical enablers such as analytics, machine learning, big data and cloud for rapid IoT services, business offerings and benefits.
A complete data set of every movement of all the inpatients from room to room covering two years was provided us by the Medical Information Department of the University of Tsukuba Hospital in Japan. By focusing on the obstetric patients, who are assumed to be hospitalized rather at random times, we have analyzed the patient flow using our original visualization software. Upon admission, each obstetric patient is assigned to a bed in one of the two wards, one for high-risk delivery and the other for normal delivery, and then she may be transferred between the two wards before discharge. We confirm Little's law of queuing theory for the patient flow in each ward. Then we propose a network model of M/G/"V and M/M/m queues to represent the flow of these patients, which is used to predict the probability distribution for the number of patients staying in each ward at the nightly census time from the observed data of patient admission rate and the histogram of the length-of-stay (LOS) in that ward. Although our model is a very rough and simplistic approximation of the real patient flow, the predicted probability distribution is shown to be in good agreement with the observed one. Our method can be used for planning the capacity of obstetric units when the patient demand is predicted.
One of the most challenging knowledge services is to provide information relevant enough to support making effective decisions in real time. Even though many sources of relevant data and knowledge are available on websites at any given time, they are scattered and offer little or no information on the semantic relationships, thus making such sources hard to exploit. This paper proposes an approach to developing a spatial and time-based advisory system by using ontology for aggregating data from heterogeneous databases, and from devices such as climate sensors and mobile phones, using shallow parsing to extract the domain-specific concepts and their attributes from semi-structured text, and using production rules to activate functional knowledge formalized from natural language text that is dispersed across the Web. Precision farming for rice is used as a case study since it relies upon intensive sensing of environmental conditions of the crop, extensive data handling and processing, and farmer knowledge. This work aims to support resource-poor farmers toward higher productivity while minimizing costs. The service offered is to therefore provide personal assistance, thus enhancing a farmer's ability to apply actions effectively according to the crop calendar, i.e. the optimal use of pesticides and nutrients in heterogeneous field situations that affect crop quality and reduce risk.
In recent times technical developments in electric mobility have been rapid. With raising economic and ecological concerns about conventional vehicles, individuals and companies need to consider switching to electric vehicles. But due to limited range and charge times such vehicles are currently not sufficient. New mobility concepts are needed as well. In this work we present the Shared E-Fleet architecture used to enable sharing of car fleets between companies. Especially tailored to the needs of SMEs, the architecture is implemented as a cloud service chain, enabling configurability and interchangeability of services.
Market globalization has brought us not only new opportunities, but also new challenges. The theme of innovation has become a mandatory topic for all industries - it has become a focal point for the enterprise, society and the world. The goal of innovation is to create business value by developing worthwhile ideas into a customer-centric marketable reality. This, for most companies, is difficult to achieve due to the lack of a methodology and tools for systematic innovative thinking. This paper introduces the concept and strategies for product-service innovation based on a Dominant Innovation Design approach. An innovation toolset that effectively combines an Innovation Matrix, Application Space Map, and QFD tools is explained. This methodology enables the identification of hidden customer needs and gaps as they relate to product-service requirements. This paper also shares two case studies of how this tool-set can be utilized for real product-service innovation. The case study includes an overall IT-enabled service solution for a traditional product, which encompasses service thinking, blueprint planning, technical solution evaluation, service platform implementation, and service operation. Combining these elements can lead manufacturing industries into new service business model based on their core product while creating more value to their customers.
Although there is a plethora of articles, conference papers on the subject of IT evaluation only a small subset has explicitly dealt with precisely what the term value means and how it can be used within IT investment decisions. Defining value is an important component of managing an investment portfolio. Drawing on practical experience and applied research in strategy development and portfolio management, this paper highlights several facets of value that are critical and practical to estimate and apply within the IT investment domain.
The success of traditional learning is based on the individual use of learning tools, depending on the learners' personality. But modern eLearning is disregarding this major fact. By using learning analytics, this paper examines the correlation between personality traits of the user and preferred learning tools in an eLearning scenario. The investigation is based on a field study with 992 participants. The participants worked on a collaborative and online-based scenario in which they were assigned with a task in groups of five students. The results of this research show that personality traits, particularly in case of eLearning, have a significant influence on the individual preference of learning tools. This finding can be used to offer learners individual learning environments.
The full paper introduces the creation process and outcome of an e-learning based course targeting small and medium-sized enterprises (SMEs). Course content is the individual electronic knowledge sharing in SMEs. The project is sponsored by the European Social funds.
In order to design future agriculture, present agriculture should be redesigned based on the context of smart agriculture that indicates the overall form of agriculture including a social system. Wireless sensor network (WSN) and the various type of optical sensors are assumed to be a basic technology of smart agriculture which intends the harmony with the economic development and sustainable agro ecosystem. In this paper, the current state and development for the environmental and plant sensing based on the next generation agricultural technology with WSN are introduced.
This paper presents a human behavior analysis model combining process discovery and decision tree analysis. The proposed analysis model uses the event log traces generated by enterprise sales applications, which are mainly used by salespeople. We verify the effectiveness of the proposed approach using scenario-based simulation and find that our proposed approach is robust against randomly generated activities. We also present that important work behavior patterns can be discovered using the process discovery technology by checking higher frequency transitions.
