The Statement:

Your team is a multi$-$disciplinary Big Data Task Force of the Company Applications of Big & Consistent Data $($ABCD$)$ with its main business in the sector of $$XYZ smart cities$.$ The Task Force members are appointed to support the ABCD Senior Steering Committee $($SSC$)$ in their intelligent business decision support at a time of business change $($such as enforced by the recovery from post$-$pandemic and world crises and progress towards a sustainable state of the national economy$).$ Along own resources, ABCD SSC would like to identify a number of public digital resources consistent and in support of own big data assets.

The Big Data Task Force team$$s second aim is to support the initial work with individual case studies as evidences on the relevant benchmarked data resource$($s$)$ that critically explore data using well$-$established, high quality references and statistical functions, models, applications and tools; identify, assess and interpret published results appropriate for ABCD new business model; and advise the ABCD Senior Steering Committee on best alternatives based on a comprehensive summary of the evidences produced.

Your contribution is to critically identify, analyse, design and develop statistical models, evaluate and report results for an individual case study relevant for the Big Data Task Force team$$s original topic of research.

This CW$2$ addresses the following Learning Outcomes:

$1$: $[..]$ critically analyse solutions for big data statistical analysis and processing $($from LO$1).$

$2$: Critically analyse available data, design experiments, develop solutions, produce and evaluate results, and guide toward appropriate suitably$-$designed applications of big data statistical analysis and learning $($LO$2).$

$3.$ Identify correlations and construct statistical models from industrial big data resources $($LO$3).$

$4.$ Interpret statistical learning model results and communicate them to the general public $($including non$-$specialists$),$ reflect and carry out a critical review of the issues related to legal, social, ethical and professional issues, including data and risk management and data protection $($LO$4).$

$5.$ Demonstrate $[..]$ of data, practical software tools and code, with a focus on workflow design, experimentation and validation.$$

Exercise CW$2$: Industrial Big Data Analytics

You $($each individual team member$)$ are expected to choose, critically explore, analyse, process, model, evaluate and interpret results for a big data resource and$/$or its analytics application$($s$)$ based on a recent research paper, for example based on existing $($open$)$ data re$/$sources$,$ and case studies covered in the module academic delivery and aligned with the initial CW$1$ topic.

In this report you will review the work covered in $($at least$)$ one relevant research paper and critically assess it against other relevant research papers of your choice.

Your report will contain the following sections:

C$1.$ Title $($one line$)$ and author.

C$2.$ Abstract $($one paragraph $5-10$ lines$)$: the review should start with a summary paragraph that describes briefly the addressed topic, problem and main challenges, and your critical evidence and review outcome.

C$3.$ Introduction to the topic: data resource$($s$),$ exploration and analytics approach or application $($a couple of paragraphs$).$

C$4.$ The report structure $($one short paragraph$).$

C$5.$ Critical Literature Review $($a couple of paragraphs rich in references$).$

C$6.$ Experiment Design, Description and Evidence of Implementation, Evaluation and Interpretation $($to support the work reported in the references of choice$)$ preferably using pseudo$-$code, flowcharts or block diagrams$).$

C$7.$ Strong Points and Achievements $(2-3$ sentences$)$: list $2-3$ strong points that you see about the paper authors$$ contributions and their proposed solution, e$.$g$.,$ why this work is novel, what is the most interesting idea behind the solution, does the paper have enough evaluation and performance measures. Critically evaluate these points using critical review of similar work reported in other $4-5$ papers.

C$8.$ Weak Points $(2-3$ points$)$: list $2-3$ weak points that you think have not been addressed adequately, possible weaknesses, assumptions that are not practical, or extensions you think are good. Critically evaluate these points using critical evaluation of similar work reported in other $4-5$ papers.

C$9.$ Comparative evaluation of results by own statistical applications you will develop to produce relevant evidence containing data statistical exploration, transformation, processing and model evaluation and potential improvement solutions, including legal, social, ethical, professional and privacy risks.

C$8.$ Conclusions $($one paragraph: $5-10$ lines$).$

C$8.$ References.

C$9.$ Appendices $($e$.$g$.$ relevant results in graphical representations or tables, code or weblinks to github folders with code and$/$or data$).$

You may find also useful the following directions and suggestions:

$$ What difficulties did you find regarding reproduction of the main research paper. $.$write this assignment on 'ambulance traffic data'