Service Analytics

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    On Predictability of Revisioning in Corporate Cash Flow Forecasting
    ( 2018-01-03) Knöll, Florian ; Setzer, Thomas ; Laubis, Kevin
    Financial services within corporations usually are part of an information system on which many business functions depend. As of the importance of forecast quality for financial services, means of forecast accuracy improvement, such as data-driven statistical prediction techniques and/or forecast support systems, have been subject to IS research since decades. In this paper we consider means of forecast improvement due to regular patterns in forecast revisioning. We analyze how business forecasts are adjusted to exploit possible improvements for the accuracy of forecasts with lower lead time. The empirical part bases on an unique dataset of experts' cash flow forecasts and accountants' actuals realizations of companies in a global corporation. We find that direction and magnitude of the final revision in aggregated forecasts can be related to suggested targets in earnings management, providing the means of improving the accuracy of longer-term cash flow forecasts.
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    The Role of Semantic Technologies in Diagnostic and Decision Support for Service Systems
    ( 2018-01-03) Tsalapati, Eleni ; Jackson, Tom ; Johnson, William ; Jackson, Lisa ; Vasilyev , Andrey ; West, Andrew ; Mao, Lei ; Davies, Ben
    In this research, we utilize semantic technology for robust early diagnosis and decision support. We present a light-weight platform that provides the end-user with direct access to the data through an ontology, and enables detection of any forthcoming faults by considering the data only from the reliable sensors. Concurrently, it indicates the actual sources of the detected faults, enabling mitigation action to be taken. Our work is focused on systems that require only real-time data and a restricted part of the historic data, such as fuel cell stack systems. First, we present an upper-level ontology that captures the semantics of such monitored systems and then we present the structure of the platform. Next, we specialize on the fuel cell paradigm and we provide a detailed description of our platform’s functionality that can aid future servicing problem reporting applications.
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    Stable Matrix Approximation for Top-N Recommendation on Implicit Feedback Data
    ( 2018-01-03) Li, Dongsheng ; Miao, Changyu ; Chu, Stephen ; Mallen, Jason ; Yoshioka, Tomomi ; Srivastava, Pankaj
    Matrix approximation (MA) methods are popular in recommendation tasks on explicit feedback data. However, in many real-world applications, only positive feedbacks are explicitly given whereas negative feedbacks are missing or unknown, i.e., implicit feedback data, and standard MA methods will be unstable due to incomplete positive feedbacks and inaccurate negative feedbacks. This paper proposes a stable matrix approximation method, namely StaMA, which can improve the recommendation accuracy of matrix approximation methods on implicit feedback data through dynamic weighting during model learning. We theoretically prove that StaMA can achieve sharper uniform stability bound, i.e., better generalization performance, on implicit feedback data than MA methods without weighting. Meanwhile, experimental study on real-world datasets demonstrate that StaMA can achieve better recommendation accuracy compared with five baseline MA methods in top-N recommendation task.
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    Towards a Technician Marketplace using Capacity-Based Pricing
    ( 2018-01-03) Wolff, Clemens ; Vössing, Michael ; Schmitz, Björn ; Fromm, Hansjörg
    Today, industrial maintenance is organized as an on-call business: Upon a customer’ s service request, the maintenance provider schedules a service technician to perform the demanded service at a suitable time. In this work, we address two drawbacks of this scheduling approach: First, the provider typically prioritizes service demand based on a subjective perception of urgency. Second, the pricing of technician services is inefficient, since services are priced on a time and material basis without accounting for additional service quality (e.g. shorter response time). We propose the implementation of a technician marketplace that allows customers to book technician capacity for fixed time slots. The price per time slot depends on the remaining capacity and therefore incentivizes customers to claim slots that match their objective task urgency. The approach is evaluated using a simulation study. Results show the capabilities of capacity-based pricing mechanisms to prioritize service demand according to customers’ opportunity costs.
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    How to Improve Cloud Services Availability? Investigating the Impact of Power and It Subsystems Failures
    ( 2018-01-03) Rosendo, Daniel ; Leoni, Guto ; Gomes, Demis ; Moreira, André ; Gonçalves, Glauco ; Endo, Patricia ; Kelner, Judith ; Sadok, Djamel ; Mahloo, Mozhgan
    The cloud data center is a complex system composed of power, cooling, and IT subsystems. The power subsystem is crucial to feed the IT equipment. Power disruptions may result in service unavailability. This paper analyzes the impact of the power subsystem failures on IT services regarding different architecture configurations based on TIA-942 standard such as non-redundant, redundant, concurrently maintainable, and fault tolerant. We model both subsystems, power and IT, through Stochastic Petri Net (SPN). The availability results show that a fault tolerant power and IT configuration reduces the downtime from 54.1 to 34.5 hours/year when compared to a non-redundant architecture. The sensibility analysis results show that the failure and repair rates of the server component in a fault tolerant system present the highest impact on overall data center availability.