# The AI-Powered Evolution of Big Data
## 4 Materials and Methods
### Big D -- The Big Data Analyzer
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\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Test Case ID** & **Test Area** & **Test Case Description** & **Expected Outcome** & **Pass/Fail Criteria** & **Pass/Fail** \\ \hline \multirow{4}{*}{TC06} & \multirow{4}{*}{Predictive analytics} & Assess Big D’s predictive analytics capabilities by providing historical data and comparing predicted outcomes to actual results. & Big D’s predictions should be accurate within a defined margin of error (e.g., \textless{5\%} deviation from actual results). & Predictions align with actual outcomes within a & \multirow{4}{*}{Predictions align with actual outcomes within a 5\% margin.} & \multirow{4}{*}{Predictions align with actual outcomes within a 5\% margin.} & \multirow{4}{*}{Predictions align with actual outcomes within a 5\% margin.} & \multirow{4}{*}{Predictions align with actual outcomes within a 5\% margin.} & \multirow{4}{*}{Predictions align with actual outcomes within a 5\% margin.} & \multirow{4}{*}{Predictions align with actual outcomes within a 5\% margin.} & \multirow{4}{*}{} \\ \cline{1-1} \cline{5-5}  & Resource allocation optimization & Test Big D’s ability to optimize resource allocation during peak data processing times. & Big D should dynamically allocate resources to minimize processing time and prevent system overload. & & \multirow{4}{*}{} & \multirow{4}{*}{} & \multirow{4}{*}{} \\ \hline \multirow{4}{*}{TC08} & \multirow{4}{*}{Data security and compliance} & Ensure Big D adheres to data privacy regulations (e.g., GDPR, CCPA) by anonymizing data fields. & Big D should anonymize all sensitive data fields according to compliance requirements, with no breaches detected. & Data are fully anonymized; no compliance & \multirow{4}{*}{} & \multirow{4}{*}{} \\ \cline{1-1} \cline{5-5}  & \multirow{4}{*}{System failover and recovery} & Simulate system failure during data processing to check Big D’s failover and recovery mechanisms. & & & & \\ \hline \multirow{4}{*}{TC10} & \multirow{4}{*}{User interface (UI) responsiveness} & Evaluate the responsiveness of Big D’s UI when queried for real-time data insights under heavy system load. & The UI should remain responsive, with query results delivered within acceptable timeframes (\textless{3 s}). & UI remains responsive; queries return results \textless{3 s}. & \multirow{4}{*}{} & \multirow{4}{*}{} \\ \hline \hline \end{tabular}
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Table 4: _Cont._word to a common base form like \"singing\", \"sang\", \"sing\" will all become \"sing\" as this is their common root and their meaning. A word cloud of the top tokens is then created and remains an integral part of Big D analysis, providing users with an immediate visual representation of key terms within their datasets. By integrating dynamic updates, advanced preprocessing, and scalable functionality, the enhanced word cloud supports more targeted and effective data analysis, allowing users to identify important keywords and topics quickly. These steps have improved Big D's capacity to handle long documents. Refining the initial document and its information provides more semantic information, and the analysis goes beyond just syntax. This processing workflow ensures that Big D can deliver more precise results and provide deeper insights into the content, enhancing its analytical capabilities. #### 4.5.1 Sentiment Analysis

A sentiment analysis feature has been integrated into the system to enhance Big D's analytical capabilities further. This functionality utilizes the VADER (Valence Aware Dictionary and Sentiment Reasoner) sentiment analyzer, a popular tool used to assess the emotional tone of textual data. Adding sentiment analysis enables Big D to provide users with deeper insights into the sentiments expressed within documents, enhancing its overall utility in text analysis and decision-making processes. The key features of sentiment analysis in Big D are as follows:

Big D evaluates the emotional tone conveyed in text data, categorizing it as positive, negative, or neutral.