Statistical Engine Type of processing Sample Clauses

Statistical Engine Type of processing. Processing method 1. Batch Processing; executing a series of programs every time inter- val, technology suggested - R software. 2. Stream Processing; processing computations parallel in memory, enables real time analytics, technology suggested - Spark Stream- ing software web application framework. 3. Hybrid Processing; combining Batch and Stream processing 6 Optimizer We will produce the optimal results according to our WP2 partners’ spec- ifications. Table 3 expands on the possible methods mentioned above in respect to the four industrial scenarios we have identified earlier. H2020-SPIRE-2014 DISIRE Table 3: Possible Technologies by Module and Scenario # Module Task Small Data; Non-Real Time Small Data; Real Time Big Data; Non-Real Time Big Data; Real Time 2 Sensor Data Files Data acquisition "Pull Based" approach "Push Based" approach "Pull Based" approach "Push Based" approach Storing sensor’s files in the hard drive in the hard drive Distributed data store in the SSD or in the RAM Eliminating re- dundancy Usually not needed filtration might be needed filtration and compression need to be consider filtration and compression need to be consider 3 Organization and preparation of the data Preparation of the data Using the entire row- data Using the entire row-data using the entire row-data or Aggregate the data need to be consider using the entire row-data or Aggregate the data need to be consider Organization of the data ETL programming ETL programming or Ob- ject oriented programming need to be consider ETL programming ETL programming or Ob- ject oriented programming need to be consider 4 Sensor Database Type of data- base RDBMS RDBMS Distributed data store Distributed data store 5 Statistical Engine Type of pro- cessing Batch Processing Stream Processing or Hy- brid Processing Batch Processing Stream Processing or Hy- brid Processing 6 Optimizer H2020-SPIRE-2014 DISIRE In the previous two sections we have introduced the basic framework that we will use as a starting point in discovering the optimal solutions in our WP. As mentioned earlier this archi- tecture must be robust in handling the various types of data we have encountered in the indus- try with our DISIRE partners. In some of the cases we have seen that historical data is kept and stored for future use while in other cases the streaming data is disregarded soon after it has been used. Our prototype is planned to be generic enough to include both cases. H2020-SPIRE-2014 DISIRE
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