Marco Pospiech

A Descriptive Big Data Model Using Grounded Theory

Big Data is an emerging research topic. The term remains fuzzy and jeopardizes to become an umbrella term. Straight forward investigations are inhibited since the research field is not well defined, yet. This paper executes expert interviews to identify a common understanding. Hereby, the findings are coded and conceptualized until a descriptive Big Data model is developed by using Grounded Theory. It becomes evident that Big Data is use case driven and forms an interdisciplinary research field. By classifying several Big Data papers it gets obvious that not all of them belong to this research field. The paper contributes to the intensive discussion about the term Big Data in illustrating the underlying area of discourse. A classification to set the research area apart from others can be achieved to support a goal oriented research in future.

Deployment of a Descriptive Big Data Model

Big Data is an emerging research topic. The term remains fuzzy and jeopardizes to become an umbrella term. Straight forward investigations are inhibited since the research field is not well defined, yet. To identify a common understanding, experts have been interviewed. Hereby, the findings are coded and conceptualized until a descriptive Big Data model is developed by using Grounded Theory. This provides the basis for the model’s deployment. Here, academic publications and practical implementations marked as Big Data are classified. It becomes evident that Big Data is use-case driven and forms an interdisciplinary research field. Even not all papers belong to this research field. The findings become confirmed by the practical implementations. The chapter contributes to the intensive discussion about the term Big Data in illustrating the underlying area of discourse. A classification to set the research area apart from others can be achieved to support a goal oriented research in future.

Big Data -- A Theory Model

Big Data is an emerging research topic. However, the term remains fuzzy and is seen as an umbrella term. Its origin, composition, possible strategies, and outcomes are unclear. This impedes the positioning of publications addressing business administration issues related to Big Data. The missing theoretical foundation of Big Data has been recognized, as has the need for underlying relationships and concepts to be elucidated. While a few publications have directly addressed this need, the analysis has remained methodically weak. Based on an existing qualitative model, we developed a Big Data theory model and tested it through structural equation modeling. All the hypotheses in our research model proved significant. The study makes three principal contributions to the scientific discussion about Big Data. First, it elucidates the current characteristics of Big Data. Second, the addressability of Big Data through strategies is demonstrated. Third, it produced evidence of positive outcomes through Big Data.

Towards a big data theory model

Big Data is an emerging research topic. The term remains fuzzy and is seen as an umbrella term. Origin, composition, possible strategies, and outcomes are uncertain. Thus, the positioning of publications addressing business administrated issues related to Big Data is impeded. From a practitioners point of view, the ability to communicate a value proposition is impeded due to the difficulty in scoping the intended artifact and the interpretation of arisen company results. So, underlying relationships and concepts have to be described. The missing theoretical fundament of Big Data has been stated in literature. While some publications actually address this need, the majority of them remain methodically weak. In a previous study we deduced an initial qualitative Big Data theory model based on expert interviews and grounded theory. It is this papers goal to verify the given model in a quantitative way and test it through structural equation modeling. Thereby, hypothesis are deduced and Big Data indicators presented. As a result, a Big Data theory model arises. All hypotheses of our research model are significant, and the study makes three principal contributions to the scientific discussion about Big Data. First, it unveils the underlying characteristics of Big Data. Second, we show the addressability of Big Data through strategies. Hereby, possible strategies to address Big Data are highlighted. Third, we found evidence that positive outcomes like return of investments through Big Data are possible. Thereby, the latter two aspects are of major interest for practice. The presented work contributes to the scientific discussion and supports a development of this domain.

Business Analytics for Price Trend Forecasting through Textual Data

Various data sources are available in the era of Big Data to increase business understanding. For example, price predictions based on news ticker offers a broad range of valuable information. An automatic analysis is able to support traders in their daily business to maximize profits. But, only little is known about this topic, yet. In cooperation with a globally acting company, we developed a generalizable approach to use news tickers for price trend forecasts. First, we realized that the effect on prices by news tickers is complex to identify. Second, irrelevant tickers decrease the performance. Several approaches are evaluated to identify relevant articles in an automatic fashion, whereby the functionality is demonstrated in two different case studies. The results are practicable. Our research contributes to the discussion about business analytics, business cases, and their realization. It can be applied in any domain where important events have to be considered instantly.

Forecasting the Natural Gas Price Trend - Evaluation of a Sentiment Analysis

Nowadays, text messages are of interest, because they quickly convey information about events. For this reason, we analyze, whether a prevailing sentiment in a text-based financial news has impact on the price trend of natural gas at an energy exchange. This prediction method supports utility companies, because it allows faster trading decisions on the natural gas market and thus reduce associated business risks. It is also transferable into other business domains. We initially applied text mining methods to gain first results and moved over to sentiment analysis (SAN) to be able to evaluate their capability to support trading decisions. The calculated performance metrics of SAN made obvious that the consideration of the sentiment in the text is suitable for identifying no price influences, but is weak for identifying the impact of text news on the price trend itself. This results demands further research on applying different approaches on text analysis.

Identification of Smart Home Potentials in Germany

Smart Homes promise a new approach in energy efficiency based on terms such as demand side management as well as increased comfort and security. While technical foundations have been laid, the market is still in its early development stage. To support penetration, it is of importance to analyze market characteristics and act respectively. This paper identifies general market characteristics that are needed for a successful market implementation. The characteristics are proofed against the German Smart Home market using a literature review and expert interviews. Based on the findings, recommendations to improve the development of this market are derived. Findings are arranged within a SWOT analysis. It becomes evident that a cooperative interaction between all players is essential to offer a broad product variety concerning different customer groups. Thus, a comprehensive analysis regarding the current state of emergence of the German market is conducted. The identified strengths and weaknesses enable a goal-oriented research in favor of a future and market development. Potential investors contribute from detected opportunities and threats. Initial strategies are given.

A Novel Approach for Decision Support in Uncertain Environments: The Case of Identifying Similar News Tickers in Natural Gas Trading

As the natural gas market is characterized by price volatility and uncertainty, the participants need in using founded information for decision making in order to manage risks and profits is immense. Analyzing the information content of news tickers can provide additional information about the environment. This paper concerns the features of a novel method that encourages price prognosis in gas trading. By knowing how the market has developed regarding a certain former situation, this knowledge can be used in predicting the future market by associating a similar former state to a present state. Thereby, uncertainty about future might be reduced. Framing the research in design science, we use task-technology-fit theory and technology-acceptance-model to identify requirements and to appraise the artifact. The novel method integrates structured and unstructured data in decision support.

Detecting similar news tickers in the area of natural gas trading: improving decision support in uncertain situations

The volatility of the natural gas market founded a need for the ability to analyze upcoming events in real time in order to manage profits and risks for participants. News ticker provide information being of utmost importance for the analysis. The research presented among this paper describes features of a software prototype supporting the analytical price prognosis tasks for gas traders. By knowing market development at the time of a certain past situation, the outcome of that situation can be used to predict the future market development of a current analyzed situation with similar content. For that, similar situations have to be detected in order to reduce uncertainty about future. Fitting into design science, we use task-technology-fit theory and technology-acceptance-model to identify information needs and to evaluate the artifact. This novel approach serves as a further step to gain a decision support with integrated structured and unstructured data.