Markus Jäger

Architecture of an Extendable and Cloud-Ready Knowledge Management and Processing Framework for the Agricultural Domain

In this paper we present a software architecture for a Knowledge Management and Processing Framework initially for usage in the agricultural domain, but customizable for any domain. In contrast to existing Knowledge Management and Processing Systems, this proposed architecture mainly focuses on the usage of a cloud platform as execution environment and therefore pays special attention to the design aspects to utilize the benefits of a cloud infrastructure, by designing the system parallelizable and distributable. We identified the main aspects of a cloud-ready system platform and combined them with the needed functionality for a custom Knowledge Management and Processing Framework.

Range-Based Clustering Supporting Similarity Search in Big Data

Thanks to state-of-the-art technologies, we have more and more modern infrastructures as well as automatic processes supporting the agricultural domain. Data collected from parcels by these systems and remote sensors for further analysis result in facing the three main challenges which are known as big volume, big variety, and big velocity, in the era of big data. In terms of similarity search, we propose a range-based clustering method that finds objects which are the most similar compared to the given object in a large-scale computing with Map Reduce. The proposed method groups objects into different clusters which are considered as pivots to perform pre-checking before computing similarity. Furthermore, we conduct some basic experiments to evaluate the performance of the proposed method and observe the influences of the clusters in similarity search.

An Efficient Document Indexing-Based Similarity Search in Large Datasets

In this paper, we principally devote our effort to proposing a novel MapReduce-based approach for efficient similarity search in big data. Specifically, we address the drawbacks of using inverted index in similarity search with MapReduce and then propose a simple yet efficient redundancy-free MapReduce scheme, which not only takes advantages over the baseline inverted index-based procedures but also adapts to various similarity measures and similarity searches. Additionally, we present other strategic methods in order to potentially contribute to eliminating unnecessary data and computations. Last but not least, empirical evaluations are intensively conducted with real massive datasets and Hadoop framework in the cluster of commodity machines to verify the proposed methods, whose promising results show how much beneficial they are when dealing with big data.

Introducing the Factor Importance to Trust of Sources and Certainty of Data in Knowledge Processing Systems - A new Approach for Incorporation and Processing

In knowledge processing systems data is gathered from several sources. After some calculating and processing steps are taken in the system, a result is finally computed and may be used for further steps or by other systems. Most of the time the origin and provenance of input data is not verified. Using unverified data can cause inconsistencies in processing and generating output, and could lead to corrupting threats for the system and the environment as a whole. We propose an approach where several characterizing values in a given environment – trust of source, certainty of data, and importance (of data) in the current processing step – are used to compute new output characteristics of a knowledge processing system. These values represent the trustworthiness and the certainty of the output in multi-step processing systems based on all used sources and input data. We demonstrate the application of our approach on simple and advanced fictitious scenarios as well as on a real world scenario from the agricultural domain. Keywords-Certainty; Importance; Knowledge; Knowledge Processing Systems; Provenance; Security; Trust;

IFIN+: A Parallel Incremental Frequent Itemsets Mining in Shared-Memory Environment

In an effort to increase throughput for IFIN, a frequent itemsets mining algorithm, in this paper we introduce a solution, called IFIN+, for parallelizing the algorithm IFIN with shared-memory multithreads. The inspiration for our motivation is that today commodity processors’ computational power is enhanced with multi physical computational units; and therefore, exploiting full advantage of this is a potential solution for improving performance in single-machine environments. Some portions in the serial version are changed in means which increase efficiency and computational independence for convenience in designing parallel computation with Work-Pool model, be known as a good model for load balance. We conducted experiments to evaluate IFIN+ against its serial version IFIN, the well-known algorithm FP-Growth and other two state-of-the-art ones FIN and PrePost+. The experimental results show that the running time of IFIN+ is the most efficient, especially in the case of mining at different support thresholds in the same running session. Compare to its serial version, IFIN+ performance is improved significantly.

Incorporating Trust, Certainty and Importance of Information into Knowledge Processing Systems – An Approach

The origin of data (data provenance), should always be measured or categorized within the context of trusting the source of data. Can we be sure that the information we receive is trustworthy and reliable? Is the source trustable? Is the data certain? And how important is the received data the our current and next step of processing? We face these questions in the context of knowledge processing systems by developing a convenient approach to bring all these questions and values – trustability, certainty, importance – into a computable, measurable, and comparable way of expression. Not yet facing the question “How to compute trust or certainty?”, but how to incorporate and process their measured values in knowledge processing systems to receive a representative view on the whole environment and its output.

Data, Information & Knowledge Sources in the Agricultural Domain

We try to make a first step towards merging sources in the agricultural domain with experts and methods from the IT sector. The result should help people in this domain to profit from a better and more productive way of using existing experiences by sharing and making them easier accessible. After a short definition of several knowledge-related terms we present existing and possibly useful standards for sources in the agricultural domain. Based on the standards, we give a short overview on existing sources and present a way for automated extraction of information and knowledge from selected sources. Finally we show the usage of some sources, which are implemented in our current research work.

Connecting small, private & independent hydro power plants to increase the overall power generating efficiency

Abstract: In countries, where many small rivers exist, the geography can be used to implement environment-friendly small hydro power plants for the generation of energy. The smaller such hydro power plants are, the higher is the impact of environmental incidents. Usually, there are more than one small hydro power plants located alongside one river, mostly operated by different owners. To increase the overall power generating efficiency of all hydro power plants alongside one river, a good communication- and cooperating concept is needed. In our work, we propose a system concept and a prototype implementation for several small, private and independent hydro power plants to increase the energy production through a networked intelligent control system. We also show possibilities for avoiding events, which usually induce downtimes of the small hydro power plants. If these events can be minimized in number and duration, the overall energy production time is higher.

Focusing on Precision- and Trust-Propagation in Knowledge Processing Systems

In knowledge processing systems, when gathered data and knowledge from several (external sources) is used, the trustworthiness and quality of the information and data has to be evaluated before continuing processing with these values. We try to address the problem of the evaluation and calculation of possible trusting values by considering established methods from known literature and recent research.

Application of a Practical Approach for Incorporating Trust and Certainty of Information into a Knowledge Processing System in the Agricultural Domain

In knowledge processing systems, data is gathered from several sources, in the system, some calculating and processing steps are taken, and finally a result is computed and may be used for further steps or other systems. Most of the time the origin of input data is not verified. Using unverified data may cause inconsistencies in processing and generating output, and could lead to corrupting threats for the system and the environment. We propose an approach, where several characterizing values in a system – trust of source and certainty (and importance) of data – are used to compute new output characteristics of a knowledge processing system. These values should represent the trustworthiness and the certainty of the output in multi-step processing systems, based on all used sources and input data. We also apply the approach in a used calculation model in the agricultural domain: the Disease Pressure Model, which predicts the potential outbreak of a disease on a special field.