Challenges in Visual Data Analysis

Abstract-Logistics and its involved parties are nowadays faced with demanding challenges, in order to fulfill their customers' needs. Hence logistics service providers are constrained to cooperate with each other, which leads to the challenge of 'understanding' each other's service descriptions and integrating the strongly differing IT-systems. An emerging approach to solve this problem is the operation of cloud platforms. Main tasks are service retrieval and composition. However, a suitable visualization is needed to generate a high user acceptance. With the service map concept a first step is taken, that certainly needs further improvement. This paper briefly gives an introduction to general information visualization and analyzes the suitability of several approaches for improving the service map concept with regards to different scales of measurement. After their comparison a general guideline for fostering the visualization concept is derived. Objective is the increase of information content while keeping an intuitive usability.

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“…The core part is constituted in data reduction techniques [15], as the amount of data easily exceeds the display capacities.…”

Section: Information Visualizationmentioning

confidence: 99%

Visual enhancement of service maps in logistics clouds

Glöckner

Schwarzbach

Barton

et al. 2014

Annals of Computer Science and Information Systems

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“…Moreover, it will provide the means to support increased collaboration among all the involved stakeholders, e.g. by allowing them to annotate and enrich the managed contents, as well as to apply information visualization techniques [4] for improving the representation and communication of the experimental results.…”

Section: Open Evaluation Infrastructurementioning

confidence: 99%

A PROMISE for Experimental Evaluation

Braschler

Choukri

Ferro

et al. 2010

Lecture Notes in Computer Science

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Abstract. Participative Research labOratory for Multimedia and Multilingual Information Systems Evaluation (PROMISE) is a Network ofExcellence, starting in conjunction with this first independent CLEF 2010 conference, and designed to support and develop the evaluation of multilingual and multimedia information access systems, largely through the activities taking place in Cross-Language Evaluation Forum (CLEF) today, and taking it forward in important new ways.PROMISE is coordinated by the University of Padua, and comprises 10 partners: the Swedish Institute for Computer Science, the University of Amsterdam, Sapienza University of Rome, University of Applied Sciences of Western Switzerland, the Information Retrieval Facility, the Zurich University of Applied Sciences, the Humboldt University of Berlin, the Evaluation and Language Resources Distribution Agency, and the Centre for the Evaluation of Language Communication Technologies.The single most important step forward for multilingual and multimedia information access which PROMISE will work towards is to provide an open evaluation infrastructure in order to support automation and collaboration in the evaluation process.

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“…As it is indicated in [4], the goal of visual analytics research is to turn the information overload into an opportunity, i.e. decision-makers should be enabled to examine massive, multidimensional, multisource, time varying information stream to make effective decisions in time critical situations.…”

Section: Introductionmentioning

confidence: 99%

Method for Visual Detection of Similarities in Medical Streaming Data

Bernatavičienė

Dzemyda

Bazilevičius

et al. 2014

INT J COMPUT COMMUN

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The analysis of medical streaming data is quite difficult when the problem is to estimate health-state situations in real time streaming data in accordance with the previously detected and estimated streaming data of various patients. This paper deals with the multivariate time series analysis seeking to compare the current situation (sample) with that in chronologically collected historical data and to find the subsequences of the multivariate time series most similar to the sample. A visual method for finding the best subsequences matching to the sample is proposed. Using this method, an investigator can consider the results of comparison of the sample and some subsequence of the series from the standpoint of several measures that may be supplementary to one another or may be contradictory among themselves. The advantage of the visual analysis of the data, presented on the plane, is that we can see not only the subsequence best matching to the sample (such a subsequence can be found in an automatic way), but also we can see the distribution of subsequences that are similar to the sample in accordance with different similarity measures. It allows us to evaluate differences among the subsequences and among the measures.

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Challenges in Visual Data Analysis

Cited by 274 publications

References 7 publications

Visual enhancement of service maps in logistics clouds

Visual enhancement of service maps in logistics clouds

A PROMISE for Experimental Evaluation

Method for Visual Detection of Similarities in Medical Streaming Data

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