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.

Software evolution analytics can be supported by generating and comparing call graph evolution information over versions of a software system. Call Graph evolution analytics can assist a software engineer when maintaining or evolving a software system. This paper proposes Call Graph Evolution Analytics to extract information from a set of Evolving Call Graphs ECG = {CG1, CG2,...CGN} representing a Version Series VS = {V1, V2,...VN} of an evolving software system. This is done using Call Graph Evolution Rules (CGERs) and Call Graph Evolution Subgraphs (CGESs). Similar to association rule mining, the CGERs are used to capture co-occurrences of dependencies in the system. Like subgraph patterns in a call graph, the CGESs are used to capture evolution of dependency patterns in evolving call graphs. Call graph analytics on the evolution in these patterns can identify potentially affected dependencies (or procedure calls) that need attention. The experiments are done on the evolving call graphs of 10 large evolving systems to support dependency evolution management. This is demonstrated with detailed results for evolving call graphs of Maven-Core’s version series.

We introduce Web Service Slicing, a technique that captures a functional subset of a large-scale web service using an interface slice captured as a WSDL slice (a subset of a service's WSDL). An interface (WSDL) slice provides access to an interoperable slice, which is a functional subset of the service's code. The technique uses intra-operational and inter-operational analysis to identify web service changes. With the aid of an associative code-test mapping, we leverage the identification of affected operations to reduce the cost of web-service regression testing by extracting a subset of the existing test cases. Used in conjunction with a web service slice, this subset reduces the cost of web-service regression testing by enabling the running of fewer tests. Furthermore, we exploit two approaches: Operationalized Regression Testing of Web Services (ORTWS) and Parameterized Regression Testing of Web Services (PRTWS). ORTWS effectively tests intra-operational changes at the WSDL and WS-code levels, while PRTWS tests inter-operational changes involving inter-operational dependencies due to primary parameters. Finally, we present results obtained using our prototype implementation, AWSCM (Automated Web Service Change Management), in two case-study experiments that serve to illustrate the reduction potential of the technique using eight real-world web services.

Entities (or components) in an evolving system keeps on evolving, which makes a state series SS = {S1, S2… SN}, where Si is the ith state of the system. There exist connections (or relationships) between entities, which also evolve over system state, and make a series of evolving networks EN = {EN1, EN2… ENN}. We can use these evolving networks to do learning over evolving system states for system evolution analysis. In this paper, we introduce a System Evolution Analytics model, which is based on proposed System Evolution Learning. The network pattern information is trained using graph structure learning. The evolution information is trained using evolution and change learning. We accomplish this by implementing a deep evolution learning. This technique uses an evolving matrix to generate evolving memory in the form of a proposed System Neural Network (SysNN). The SysNN is useful to predict and recommend based on system evolution learning. The technique is prototyped as a tool, which is used to do experiments on six evolving systems. We applied our tool to do system evolution analysis. The experiments are conducted to generate and report evolving memory as SysNN that helps to do recommendation about system.

There are many entities (or components) in a system that keeps on evolving over system states. The connection (or relationship) between entities also keep on evolving over system state, which makes series of evolving networks. Such networks can be studied over evolving state to provide system evolution information for analysis. This can be achieved with the help of hybrid mining approaches. The network rule information can be detected using network rule mining. The network subgraph information can be retrieved using network subgraph mining. The evolution information is detected using evolution mining. In this paper, we introduce a “System Evolution Analytics” model, which is explained using two pattern-mining techniques: network evolution rule mining and network evolution subgraph mining. The first technique retrieves network evolution rules (NERs), and the second technique retrieves network evolution subgraphs (NESs). The two techniques are prototyped as two System Evolution Analytics tools that are used to do experiments on six evolving systems. We demonstrated the application of the tools for the system evolution analysis.

Service analyzer requires automated approach to access Subset Service, so that cost can be reduced by organizing the test scenarios. Emphasis of analyzer is to access and handle subset of code. This paper proposed a conceptual model that access Subset Service in two steps. First, slicing the Web service based on Subset WSDL (SWSDL) to access Subset Service. Second, the Subset Service is categorized into five layers. This improves and optimizes cost of Web service analysis by slicing and layering the Service. We conducted case studies for few Service projects. SWSDLs are used for Operationalized and Parameterized Web service analysis.

Automated Web Service Change Management (AWSCM) is a pioneer tool which constructs Subset WSDL based on change impact analysis of WSDL and WS code. AWSCM visualize and capture changes in the form of intermediate artifacts during impact analysis. This paper presents AWSCM modules, intermediate artifacts, components, and its applications. The paper gives insight on computation of change impact as well as mapping them to their test cases. Discussion on details of algorithm for the construction of reduce regression test suite. AWSCM can also be useful for top down development of web services using the subset operations in WSDL.