The 17th International Conference on Knowledge Management was held in the historic city of Potsdam, Germany. Since the conference was among the first post-pandemic face to face conferences, the overall theme of the 17th edition of the ICKM conference rightly focused on “Knowledge, Uncertainty and Risks: From individual to global scale” at different levels of analysis and agency. This document highlighted one of the panels that provide an overview of the different methodologies and theories of sense-making from several of the seminal originators of sensemaking.

Global warming is the term used to describe critical environmental issues and concerns. Social media such as Twitter provides a platform for people to share information, exchange ideas, and express their opinions about current and timely issues. This study utilized contextual analysis to analyze data collected from Twitter for the hashtag "global warming" during the period 2010 & 2011. Using sentiment analysis and topic modeling, the study aimed first at assessing people's perception towards global warming issues, and second study the impact of misinformation from the standpoint of social noise on people's perception of global warming during critical environmental events. The outcome of this study helps create a better understanding of the environmental issues discussed on social media. The sentiment analysis from the data analyzed so far shows that most of the tweets were based on Twitter users' personal opinions and not science. The topic modeling results suggest that Twitter users typically tweeted when a major environmental event occurred due to global warming. Topic modeling also aids in the identification of terms that is associated with social noise. The presence of social noise suggests that misinformation does exist and spreads faster.

This paper will report on a research to develop Speaker Independent, Continuous Speech Recognizer for Kafi Noonoo (Afro-Asiatic language that belongs to North Omotic sub family in Ethiopia) using Hidden Markov Modeling technique. The portable and open source toolkit called Hidden Markov Model (HMM) Toolkit is used to perform the experiment. The development of HMM based Automatic Speech Recognition (ASR) requires both text and speech corpus for training and testing the HMM. In order to have a model that incorporates different features of the language, we included the different dialects of Kafi Noonoo in the corpus and then prepared the training and test corpus from the scratch, and after preprocessing we have sampled and performed feature extraction using Mel Frequency Cepstral Coefficients (MFCC) feature extraction technique.

Building predictive models for companies often relies on inference using historical data of companies in the same industry sector. However, companies are similar across a variety of dimensions that should be leveraged in relevant prediction problems. This is particularly true for large, complex organizations which may not be well defined by a single industry and have no clear peers. To enable prediction using company information across a variety of dimensions, we create an embedding of company stocks, Stock2Vec, which can be easily added to any prediction model that applies to companies with associated stock prices. We describe the process of creating this rich vector representation from stock price fluctuations and characterize what the dimensions represent. We then conduct comprehensive experiments to evaluate this embedding in applied machine learning problems in various business contexts. Our experiment results demonstrate that the four features in the Stock2Vec embedding can readily augment existing cross-company models and enhance cross-company predictions.

Poster paper for the 2017 International Conference on Knowledge Management. This paper proposes a dimensional model with "aspects of a corpus, such as topic or opinion, as derived star schema dimensions." Focusing on a case study on Twitter the paper demonstrates how the derived dimensions, combined with transactional facts and derived facts can uncover "the collective tacit knowledge in Twitter communities."

Presentation for the 2017 International Conference on Knowledge Management. This presentation discusses the use of Twitter as a tool to predict the opinion leaders that influence the general users in relation to the 2016 U.S. Presidential Election.

Considerable research has been conducted on the topic of information overload using different approaches, from marketing and customer demand to information technologies and sciences, and even among mental health professionals. In business the critical question is how does information overload impact processes, operations, and profitability, and how can data visualization help to solve issues with data management and consumption in organizations. The ability to quickly and effectively process information and make decisions equates to organizational survival in a dynamic, knowledge-based economy where all segments of society are heavily affected by information technologies and systems and data management industries. The growing number of systems apparatuses challenges both individuals and organizations, resulting in reports of fatigue and experiences that compromise successful performance. The objective of this literature review is to discuss how data visualization tools help address information overload and optimize decision making and the business intelligence process in organizations. It concludes that data visualization, indeed, is critical in helping individuals capture, manage, organize, visualize, and present understandable data, but that decision making is affected by cognitive factors that interfere with data processing and interpretation in decision makers.

The unquestionable demand for data generation and consumption in organizations in this century prompt an increasingly notion of data as a valuable asset, provoking the urgency for reliable analysis and reporting of complex data assets towards a critical business decision making process. Computing languages like SQL, Sequential Query Language, Data Mining and Warehouse, and data visualization tools are some examples of how information technology is being applied to the problem solving strategy related to data governance and management. The objective of this article is to provide an overall framework for effective data governance, through the examination of the hierarchy data-wisdom, that can be applied to business processes improvement and business intelligence approaches. Its purpose is to present roadmaps that will lead to superior management decisions impacting the pipeline and the relationship between leaders, staffs, stakeholders, and the corporate governance in terms of data processing and consumption. This literature review work presents the argument that effective use of information technologies, the consolidation of a learning culture, and the fostering of a technological framework that is insightful and enabler of a responsible and steward data management process have decisive impact in the business processes operations and strategies in which leadership plays a …

Presentation for the 2017 International Conference on Knowledge Management. This presentation describes a study comparing the use of paired medical hashtags on Twitter and the statistical structure of the components in the hashtag trails.

Wisdom is defined as “a uniquely human quality demonstrated through an ability to apply self-insight, experience, and sound judgment in conjunction with applicable data, information, and knowledge to create a course of action leading to beneficial and productive decisions for both individuals and society” (Allen et al., 2020, p. 159). Using this definition as a foundation, this research explores how veterans use wisdom to enhance the workforce through perspective, experience, and resilience.