1. Computing & Information Sciences Kansas State University An Overview of Big Data Analytics: Challenges & Selected Applications Guest Seminar Drake University Ed Tech Suitable for your task if Have a cluster Working with large dataset Working with independent data (or assumed) Can be cast into map and reduce © 2006, H. Setiawan, National University of Singapore http://bit.ly/mapreduce-intro-setiawan

2. Computing & Information Sciences Kansas State University An Overview of Big Data Analytics: Challenges & Selected Applications Guest Seminar Drake University Ed Tech Data Quality: Desired Properties  Accuracy  Precision  Reliability  Consistency  Ease of interpretation Kinds of Uncertainty  Ignorance (epistemic, systematic)  Statistical (aleatoric) Sources of Uncertainty  Science & engineering: observational error aka measurement error  Algorithmic (estimation, approximation, quantization error)  Insufficient data (interpolation error)  Surveys: sample, measurement, coverage, non-response On Data Quality: Veracity As “Fourth V” “Batman on Big Data”, © 2013 D. Laney http://blogs.gartner.com/doug-laney/batman-on-big-data/

3. Computing & Information Sciences Kansas State University An Overview of Big Data Analytics: Challenges & Selected Applications Guest Seminar Drake University Ed Tech Task  Given: multiple, heterogeneous data sources  Produce: unified view of data  Interfaces to each source Heterogeneous Data: Differences  Scale  Rate  Type  Meaning (semantic heterogeneity) Alternative Target Schemas  Mediated schema with data adapters (“wrappers”): above  Data warehouse: Extraction, Transformation, Loading Data Integration Data Integration © 2006, Wikipedia http://bit.ly/wikipedia-data-integration

4. Computing & Information Sciences Kansas State University An Overview of Big Data Analytics: Challenges & Selected Applications Guest Seminar Drake University Ed Tech Hadoop: How MapReduce is Implemented Figure © 2011, R. Kalakota http://bit.ly/hadoop-stack-kalakota