Using Apache Solr for Images as Big Data: Presented by Kerry Koitzsch, Wipro Technologies
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1) The document describes a case study using Apache Solr for image analysis as part of a "images as big data" application prototype. Solr provides data storage and search capabilities for the Image as Big Data Toolkit. 2) Various types of data visualization are discussed, including traditional statistical charts, tabular displays, notebook-based visualization, and map-based displays. Crime data and microscope image analysis are used as examples. 3) Solr integrates well into the data pipeline due to its flexibility and ability to work with other components like Apache Tika. Deep learning and machine learning can also be incorporated to develop analytics applications with intelligent search.
Using Apache Solr for Images as Big Data: Presented by Kerry Koitzsch, Wipro Technologies
- 1. O C T O B E R 1 1 - 1 4 , 2 0 1 6 • B O S T O N , M A
- 2. Using Apache Solr for Images As Big Data: A Case Study Kerry Koitzsch Architect, Wipro Technologies
- 3. Overview of this Presentation • This quick overview of one of our ongoing projects describes why Lucene and Solr are key parts of our ongoing research, development, and client support activities. • The presentation highlights areas of research which involve Solr technologies in the “images as big data” arena: an automated microscope slide application prototype as well as other kinds of data analysis and visualization. The use case described relies heavily on Lucene, Solr, and related “helper libraries” to provide data storage capabilities for the software toolkit, the “Image as Big Data Toolkit” (IABDT). • Throughout the presentation we discuss how the flexibility, high performance, and ability to “play well with” other components makes Lucene/Solr an essential part of the application described here.
- 4. 4 01 Use Case Overview: How Solr Technologies Relate To: § ‘Old School’ statistical displays § Web-based data visualization § ‘Glue Ware’ § A crime statistic visualization § An image as big data visualization
- 5. 5 02 Types of Data Visualization Statistical displays --- ‘old school’ histogram, pie chart, and time series Tabular displays --- stylized table-based visualization with search, etc. Notebook based visualization Map based displays with geo-location Images with overlays Constructing data visualizers with Lucene | Solr components
- 6. 6 03 “Old School” Statistical Visualization Histograms, line charts, pie charts and time series displays. Notebook technologies, built-in visualization capabilities (such as Elasticsearch-Kibana or Apache Mahout visualization) may be used with Cassandra data and with Lucene/Solr. A standard ETL approach may be used as part of the data pipeline, and intelligent search can be provided by Lucene/Solr.
- 7. 7 01 “Old School” Statistical Visualization: Standard Plots and Charts
- 8. 8 01 “Old School” Visualization of Classifier Results
- 9. 9 01 “Old School” Statistical Visualization: Standard Time Series Plots
- 10. 10 01 Tabular Display Visualization: Hive Notebook
- 11. 11 01 Graph Visualization ID,Case Number,Date,Block,IUCR,Primary Type,Description,Location Description,Arrest,Domestic,Beat,District,Ward,Community Area,FBI Code,X Coordinate,Y Coordinate,Year,Updated On,Latitude,Longitude,Location9955810,HY144797,02/08/2015 11:43:40 PM,081XX S COLES AVE,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0422,004,7,46,18,1198273,1851626,2015,02/15/2015 12:43:39 PM, 41.747693646,-87.549035389,"(41.747693646, -87.549035389)"9955861,HY144838,02/08/2015 11:41:42 PM,118XX S STATE ST,0486,BATTERY,DOMESTIC BATTERY SIMPLE,APARTMENT,true,true, 0522,005,34,53,08B,1178335,1826581,2015,02/15/2015 12:43:39 PM, 41.679442289,-87.622850758,"(41.679442289, -87.622850758)"9955801,HY144779,02/08/2015 11:30:22 PM,002XX S LARAMIE AVE,2026,NARCOTICS,POSS: PCP,SIDEWALK,true,false, 1522,015,29,25,18,1141717,1898581,2015,02/15/2015 12:43:39 PM, 41.87777333,-87.755117993,"(41.87777333, -87.755117993)"9956197,HY144787,02/08/2015 11:30:23 PM,006XX E 67TH ST,1811,NARCOTICS,POSS: CANNABIS 30GMS OR LESS,STREET,true,false,0321,,6,42,18,,,2015,02/15/2015 12:43:39 PM,,, 9955846,HY144829,02/08/2015 11:30:58 PM,0000X S MAYFIELD AVE,0610,BURGLARY,FORCIBLE ENTRY,APARTMENT,false,false,1513,015,29,25,05,1137239,1899372,2015,02/15/2015 12:4 § Leveraging Graph databases and graph visualization toolkits with Lucene/Solr-centric systems § Giraph, neo4j, OrientDB, and other graph databases in combination with a Lucene/Solr centric technology stack § For example, Chicago crime data format as CSV:
- 12. Graph Visualization in Neo4J Graph Visualization Example I: Neo4J (Separate Nodes)
- 13. Graph Visualization Example : Simple UIs and Hierarchies Graph Visualization Example II: gojs Visualization
- 14. Notebook-Based Visualization Jupyter or Zeppelin notebook technologies may be used to display Solr based information and analytics results These notebook technologies can be used as the display component in a data pipeline oriented processing architecture Solr works well as one element of such a data pipeline Spring, Spring Data, and Apache Tika may be used as data pipeline components Simpler data pipelines may be evolved into Complex Event Processors (CEPs)
- 15. Notebook Visualization: Architecture and Strategy § A relatively simple data pipeline system may be build using Zeppelin notebook as a visualization of the output results § Geolocation data may be visualized as in the following example Hadoop HBase NGData Lily Solr Lucene Solandra Katta Cassandra ELK Stack Kafka Apache Spark Mesos Akka Technology components
- 16. Notebook Based Visualization: Example: Solr-Zeppelin-Cassandra
- 17. Map / Geolocation Visualization Crime data can easily be imported into Solr The data may be manipulated and pushed into Elasticsearch or Solr or back to Cassandra Elasticsearch data can be visualized using Kibana and searched compatibly with Lucene | Solr and the other modules Logstash may be used to assist in importing data from “log file analysis” type applications, or Flume or any of the many other import frameworks: Apache Tika is especially useful as a support library
- 18. Map / Geolocation Data: Crime Data in Solr § Technology stack includes the ELK Stack plus Cassandra plus Lucene/Solr/ Hadoop § Data may use CSV crime data files as an original data source § Solr can process JSON based data with geolocation data associated with it, and is especially powerful with Apache Tika
- 19. Map / Geolocation : Crime Data in Kibana § Technology stack includes the ELK Stack plus Cassandra plus Lucene/Solr/ Hadoop § Data may use CSV crime data files as an original data source § Kibana can process JSON based data with geolocation data associated with it, as can Lucene/Solr/Tika
- 20. Map | Geolocation Visualization: Data to Image
- 21. “Image as Big Data” Visualization A data pipeline with images as a data source Feature extraction can identify features of interest and write them to Cassandra as feature descriptors, using Lucene/Solr for intelligent search capability Deep learning and machine learning can enhance the processing pipeline
- 22. Image as Big Data Analysis Image as Big Data Analysis (Poggio’s MIT Vision Machine) Original Images Color Analyzers Texture Analyzers Edge Detectors Motion Analyzers Stereo Image Analyzers Discontinuity Map Generation (Including Line & Continuous Process) Cooperating Recognition Process Analysis Result Repository
- 23. Intelligent Search with Lucene Solr Centric Architecture
- 24. Image “As Big Data” Analytics Visualization: Linear Features
- 25. Automated Microscopy : The Original Components
- 26. Feature Extraction : Original Electron Microscope Image
- 27. Feature Extraction : Image to Data : Ellipses
- 28. Feature Extraction : Image to Data : Contours
- 29. “Image as Big Data” Visualization: Optical Microscope Hardware
- 30. Microscope Control Software, with Data Ingestion
- 31. “Image as Big Data” Visualization: Solr Search: Metadata
- 32. “Image as Big Data” Visualization: Microscopy UI
- 33. Another View of the Data Pipeline Image and Metadata Input Sources (or “smart sensors”) Multi-‐sensor Fusion Software Engine Short Term Computation Result Repository Long-‐Term Result Data Repository Feature Extraction and Model Builder Global System Controller
- 34. Conclusions and Future Work A use case was described in which we use a Lucene/Solr- centric technology stack to provide an intelligent search component Flat files, HDFS files, CSV data, data streams and other data sources may be used, including microscope images of many different formats, resolutions, and metadata content “Images as big data” is a viable strategy for building image processing applications with Lucene/Solr as an intelligent search component, because of Lucene/Solr’s flexibility and ability to play well with other components Deep learning, machine learning, data mining, and hybrid techniques can be used to develop Lucene/Solr-centric analytics applications with “intelligent search” capabilities Your Questions? Kerry.koitzsch@wipro.com