The COVID-19 pandemic has significantly impacted the global population, and understanding the spread and impact of the disease is crucial to managing the crisis. In this paper, we propose the Link Ontology Analytics approach for creating an RDF (Resource Description Framework) dataset of COVID-19 data to facilitate knowledge discovery and improve the decision-making process. The dataset includes confirmed cases, deaths, recoveries, and testing data from various sources. First step pre-processes the collected big data, using Natural Language Processing (NLP) techniques to extract relevant information from unstructured data sources. Second step uses rule mining to retrieve HeathCare Rules and ontologies to represent the big data in a consistent format and to provide a common vocabulary for querying the big data. Third, our resulting dataset provides a comprehensive and structured representation of COVID-19 data that can be used to analyze HeathCare Rules and relationships related to the spread and impact of the disease. We evaluate the dataset by performing various queries and analyzing the results to demonstrate its usefulness for knowledge discovery and decision-making.

Bharat (India) is an ancient land where oldest Sanskrit literatures are discovered including Vedas, Puranas, Upanishads, etc., collectively these sacred scriptures (i.e, Ved) can be called as Ancient Indian Scriptures (AIS). Understanding vast Sanskrit literature (big textual data) is a laborious task for a group of persons during their life-time. In this work, we have curated several AIS into a repository. We propose a Vedalytics (Ved Analytics) approach to retrieve Scripture Rules and concepts from ancient-words in AIS. This provides insightful knowledge about the content of each AIS. We have analysed translations of verses (Shlokas) of 4 Vedas (Rigveda, Yajurveda, Samaveda, and Atharvaveda); Shlokas of Bhagavad Gita and Ramayana; and translations of Upanishad; translations of 3 Puranas; 7 different compositions and commentaries on AIS. We retrieved rules on these multi-lingual AIS (Sanskrit, Hindi, and English) for knowledge discovery such as: a) interesting concepts and b) transitivities and lattices relationship between ancient-words. We present metrics for Coverage, Information Compression, and Knowledge Discovery to measure the efficiency of summarization. We found that our approach generates specific and contextual results as compared to generic LLMs. Repository link: https://github.com/animesh88/AncientTextAnalytics

In the financial market, Bitcoin analytics has gained lots of attention due to its high-risk high-reward nature. It is interesting to find better techniques to analyze and predict the Bitcoin price change. In this paper, we propose Bitcoin Evolution Analytics, which aims to predict the Bitcoin price change after one hour as Bearish or Bullish. For the prediction, the approach combines the Sentiment analysis and the Technical indicators. For Sentiment analysis of tweets related to Bitcoin, the approach uses three Natural Language Processing (NLP) libraries, namely VADER, FinBERT, and TextBlob, which generated eight different sentiment scores. For Technical indicators, the approach used three features of Bitcoin: User Sentiment Score, Aroon Indicators, and Accumulation/Distribution Line Indicators. We represented all these features of Bitcoin Data over time, which created a novel Bitcoin State Series. To predict the price change of the next hour as Bearish or Bullish, we built the state series for each hour of continuous 13 months (March 2021 - March 2022). To find the most reliable set of features, we have trained 27 ML models. For each feature set, we compared the average and maximum of the accuracies and f-measures. The results of our experiment show that considering the followers of the user as the "weight" of the sentiment gives a more accurate prediction. We found that a combination of Sentiment Analysis and Technical Indicators performs better than using only Sentiment Analysis.

Hate speech classification has become an important problem due to the spread of hate speech on social media platforms. For a given set of Hate Terms lists (HTs-lists) and Hate Speech data (HS-data), it is challenging to understand which hate term contributes the most for hate speech classification. This paper contributes two approaches to quantitatively measure and qualitatively visualise the relationship between co-occurring Hate Terms (HTs). Firstly, we propose an approach for the classification of hate-speech by producing a Severe Hate Terms list (Severe HTs-list) from existing HTs-lists. To achieve our goal, we proposed three metrics (Hatefulness, Relativeness, and Offensiveness) to measure the severity of HTs. These metrics assist to create an Inter-agreement HTs-list, which explains the contribution of an individual hate term toward hate speech classification. Then, we used the Offensiveness metric values of HTs above a proposed threshold minimum Offense (minOffense) to generate a new Severe HTs-list. To evaluate our approach, we used three hate speech datasets and six hate terms lists. Our approach shown an improvement from 0.845 to 0.923 (best) as compared to the baseline. Secondly, we also proposed Stable Hate Rule (SHR) mining to provide ordered co-occurrence of various HTs with minimum Stability (minStab). The SHR mining detects frequently co-occurring HTs to form Stable Hate Rules and Concepts. These rules and concepts are used to visualise the graphs of Transitivities and Lattices formed by HTs.

System Evolution Analytics on a system that evolves is a challenge because it makes a State Series SS = {S1, S2…SN} (i.e., a set of states ordered by time) with several inter-connected entities changing over time. We present stability characteristics of interesting evolution rules occurring in multiple states. We defined an evolution rule with its stability as the fraction of states in which the rule is interesting. Extensively, we defined stable rule as the evolution rule having stability that exceeds a given threshold minimum stability (minStab). We also defined persistence metric, a quantitative measure of persistent entity-connections. We explain this with an approach and algorithm for System Network Analytics (SysNet-Analytics), which uses minStab to retrieve Network Evolution Rules (NERs) and Stable NERs (SNERs). The retrieved information is used to calculate a proposed System Network Persistence (SNP) metric. This work is automated as a SysNet-Analytics Tool to demonstrate application on real world systems including: software system, natural-language system, retail market system, and IMDb system. We quantified stability and persistence of entity-connections in a system state series. This results in evolution information, which helps in system evolution analytics based on knowledge discovery and data mining.