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Published byKerry Arnold Modified over 8 years ago
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TEMPORAL DATA AND REAL-TIME ALGORITHMS AJ Jicha - Presenter Ryan Jicha - Presenter Ian Kaufer - Slide Maker Roy Zacharias - Slide Maker Frontiers in Massive Data Analysis Chapter 4, Pages 37-41 Group 3
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Agenda Topic Overview Data Acquisition Processing, Representation and Inference System and Hardware Challenges
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Topic Overview Temporal data - data which depends on time Advertising Google Maps: Imaging & mapping with real-time traffic folding@home: Protein folding research Cybersecurity (Security Information and Event Management Systems) Shift in computing environment Distributed computing
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Data Acquisition Various sources of data Different locations/destinations Processing requirements based on types of data Scheduling theories: Hard real-time Firm real-time Soft real-time Bounded-tardiness
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Processing High-speed data streams may exceed processing capacity Algorithms can be used to guess the missed data Representation Coding vs sketching Inference Algorithms used to guess answers based on real-time data Processing, Representation, Inference
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System and Hardware Distributed file systems are necessary Google’s file system (GFS), which is proprietary Large quantity of data-acquisition machines to funnel ingest to processors Numerous engineers for system support
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Major Challenges Algorithm design for massively distributed data that can adapt over time Algorithms that work on many platforms Distributed real-time acquisition, storage, transmission Consistency
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Infrastructure – Systems, Hardware, & Software Summary Data acquisitionProcessingRepresentationInferencing
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Terminology Inference Problem of turning data into knowledge using models Provenance Inferences on previously made inferences Temporal data Real-time, human-generated or measurements ...
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