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In the context of modern mobility, topics such as smart-cities, Car2Car-Communication, extensive vehicle sensor-data, e-mobility and charging point management systems have to be considered. These topics of modern mobility often have in common that they are characterized by complex and extensive data situations. Vehicle position data, sensor data or vehicle communication data must be preprocessed, aggregated and analyzed. In many cases, the data is interdependent. For example, the vehicle position data of electric vehicles and surrounding charging points have a dependence on one another and characterize a competition situation between the vehicles. In the case of Car2Car-Communication, the positions of the vehicles must also be viewed in relation to each other. The data are dependent on each other and will influence the ability to establish a communication. This dependency can provoke very complex and large data situations, which can no longer be treated efficiently. With this work, a model is presented in order to be able to map such typical data situations with a strong dependency of the data among each other. Microservices can help reduce complexity.
After kidney transplantation graft rejection must be prevented. Therefore, a multitude of parameters of the patient is observed pre- and postoperatively. To support this process, the Screen Reject research project is developing a data warehouse optimized for kidney rejection diagnostics. In the course of this project it was discovered that important information are only available in form of free texts instead of structured data and can therefore not be processed by standard ETL tools, which is necessary to establish a digital expert system for rejection diagnostics. Due to this reason, data integration has been improved by a combination of methods from natural language processing and methods from image processing. Based on state-of-the-art data warehousing technologies (Microsoft SSIS), a generic data integration tool has been developed. The tool was evaluated by extracting Banff-classification from 218 pathology reports and extracting HLA mismatches from about 1700 PDF files, both written in german language.
This paper deals with new job profiles in libraries, mainly systems librarians (German: Systembibliothekare), IT librarians (German: IT-Bibliothekare) and data librarians (German: Datenbibliothekare). It investigates the vacancies and requirements of these positions in the German-speaking countries by analyzing one hundred and fifty published job advertisements of OpenBiblioJobs between 2012-2016. In addition, the distribution of positions, institutional bearers, different job titles as well as time limits, scope of work and remuneration of the positions are evaluated. The analysis of the remuneration in the public sector in Germany also provides information on demands for a bachelor's or master's degree.
The average annual increase in job vacancies between 2012 and 2016 is 14.19%, confirming the need and necessity of these professional library profiles.
The higher remuneration of the positions in data management, in comparison to the systems librarian, proves the prerequisite of the master's degree and thus indicates a desideratum due to missing or few master's degree courses. Accordingly, the range of bachelor's degree courses (or IT-oriented major areas of study with optional compulsory modules in existing bachelor's degree courses) for systems and IT librarians must be further expanded. An alternative could also be modular education programs for librarians and information scientists with professional experience, as it is already the case for music librarians.
In industrial production facilities, technical Energy Management Systems are used to measure, monitor and display energy consumption related information. The measurements take place at the field device level of the automation pyramid. The measured values are recorded and processed at the control level. The functionalities to monitor and display energy data are located at the MES level of the automation pyramid. So the energy data from all PLCs has to be aggregated, structured and provided for higher level systems. This contribution introduces a concept for an Energy Data Aggregation Layer, which provides the functionality described above. For the implementation of this Energy Data Aggregation Layer, a combination of AutomationML and OPC UA is used.
Self-directed learning is an essential basis for lifelong learning and requires constantly changing, target groupspecific and personalized prerequisites in order to motivate people to deal with modern learning content, not to overburden them and yet to adequately convey complex contexts. Current challenges in dealing with digital resources such as information overload, reduction of complexity and focus, motivation to learn, self-control or psychological wellbeing are taken up in the conception of learning settings within our QpLuS IM project for the study program Information Management and Information Management extra-occupational (IM) at the University of Applied Sciences and Arts Hannover. We present an interactive video on the functionality of search engines as a practical example of a medially high-quality and focused self-learning format that has been methodically produced in line with our agile, media-didactic process and stage model of complexity levels.
In huge warehouses or stockrooms, it is often very difficult to find a certain item, because it has been misplaced and is therefore not at its assumed position. This position paper presents an approach on how to coordinate mobile RFID agents using a blackboard architecture based on Complex Event Processing.
Automatic classification of scientific records using the German Subject Heading Authority File (SWD)
(2012)
The following paper deals with an automatic text classification method which does not require training documents. For this method the German Subject Heading Authority File (SWD), provided by the linked data service of the German National Library is used. Recently the SWD was enriched with notations of the Dewey Decimal Classification (DDC). In consequence it became possible to utilize the subject headings as textual representations for the notations of the DDC. Basically, we we derive the classification of a text from the classification of the words in the text given by the thesaurus. The method was tested by classifying 3826 OAI-Records from 7 different repositories. Mean reciprocal rank and recall were chosen as evaluation measure. Direct comparison to a machine learning method has shown that this method is definitely competitive. Thus we can conclude that the enriched version of the SWD provides high quality information with a broad coverage for classification of German scientific articles.
We present a simple method to find topics in user reviews that accompany ratings for products or services. Standard topic analysis will perform sub-optimal on such data since the word distributions in the documents are not only determined by the topics but by the sentiment as well. We reduce the influence of the sentiment on the topic selection by adding two explicit topics, representing positive and negative sentiment. We evaluate the proposed method on a set of over 15,000 hospital reviews. We show that the proposed method, Latent Semantic Analysis with explicit word features, finds topics with a much smaller bias for sentiments than other similar methods.
Regional Innovation Systems describe the relations between actors, structures and infrastructures in a region in order to stimulate innovation and regional development. For these systems the collection and organization of information is crucial. In the present paper we investigate the possibilities to extract information from websites of companies. First we describe regional innovation systems and the information types that are necessary to create them. Then we discuss the possibilities of text mining and keyword extraction techniques to extract this information from company websites. Finally, we describe a small scale experiment in which keywords related to economic sectors and commodities are extracted from the websites of over 200 companies. This experiment shows what the main challenges are for information extraction from websites for regional innovation systems.
Lemmatization is a central task in many NLP applications. Despite this importance, the number of (freely) available and easy to use tools for German is very limited. To fill this gap, we developed a simple lemmatizer that can be trained on any lemmatized corpus. For a full form word the tagger tries to find the sequence of morphemes that is most likely to generate that word. From this sequence of tags we can easily derive the stem, the lemma and the part of speech (PoS) of the word. We show (i) that the quality of this approach is comparable to state of the art methods and (ii) that we can improve the results of Part-of-Speech (PoS) tagging when we include the morphological analysis of each word.