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Even more » Account Options. Sign in; Search settings.How often, or on what system, the Thought Police plugged in on any individual wire was guesswork. It was even conceivable that they watched everybody all of the time. But at any rate they could plug in your wire whenever they wanted to.”George Orwell, 1. On a less intrusive note, large data visualization is also of interest to anyone dealing with BI or just fascinated with massive public data sets such as twitter universe. Web maps are the way to go for public distribution and all web apps face the same set of issues when dealing with large data sets. Latency of data storage queries, typically SQL2. Latency of services for mediating queries and data between the UI and storage. Latency of the internet. Latency of client side rendering. All web map javascript APIs have these same issues whether it’s Google, Map. Quest, Nokia Here, or Bing Maps. This is a Bing Maps centric perspective on large data mapping, because Bing Maps has been the focus of my experience for the last year or two. Web Mapping Limitation. Bing Maps Ajax v. Microsoft’s javascript API for web mapping applications. It offers typical Point (Pushpin), Polyline, and Polygon vector rendering in the client over three tile base map styles, Road, Aerial, and Aerial. With. Labels. Additional overlay extensions are also available such as traffic. Like all the major web map apis, vector advantages include client side event functions at the shape object level. Although this is a powerful mapping API rendering performance degrades with the number of vector entities in an overlay. Zoom and pan navigation performs smoothly on a typical client up to a couple of thousand points or a few hundred complex polylines and polygons. Beyond these limits other approaches are needed for visualizing geographic data sets. This client side limit is necessarily fuzzy as there is a wide variety of client hardware out there in user land, from older desktops and mobile phones to powerful gaming rigs. Large data Visualization Approaches. Tile Pyramid – The Bing Maps Ajax v. API offers a tile. Layer resource that handles overlays of tile pyramids using a quadkey nomenclature. Data resources are precompiled into sets of small images called a tile pyramid which can then be used in the client map as a slippy tile overlay. This is the same slippy tile approach used for serving base Road, Aerial, and Aerial. With. Labels maps, similar to all web map brands. Fig 2 - example of quadkey png image names for a tile pyramid. Pro. A common approach is to provide dynamic tile creation with SQL or file based caching. Once a tile has been requested it is then available for subsequent queries directly as an image. This allows lower levels of the tile pyramid to be populated only on demand reducing the amount of storage required. Pro: Can handle larger number of query permutations. Server side latency is reduced by caching tile pyramid images (only the first request requires generating the image)Internet streaming is reduced to a limited set of png tile images. Client side rendering is reduced to a small set of images in the overlay. Con: Static data – dynamic data must still be refreshed in the cache. Tile creation performance is limited by server capability and can be a problem with public facing high usage websites. Hybrid - This approach splits the zoom level depth into at least two sections. The lowest levels with the largest extent contain the majority of a data set’s features and is provided as a static tile pyramid. The higher zoom levels comprising smaller extents with fewer points can utilize the data as vectors. A variation of the hybrid approach is a middle level populated by a dynamic tile service. Fig 3 – Hybrid architecture. Pro: Fast performance – although not as fast as a pure static tile pyramid it offers good performance through the entire zoom depth. Allows fully event driven vectors at higher zoom levels on the bottom end of the pyramid. Con: Static data at larger extents and lower zoom levels. Event driven objects are only available at the bottom end of the pyramid. Example: sample site and demo video Fig 4 - Example of a tile. Layer view - point data for earthquakes and Mile Markers. Fig 5 - Example of same data at a higher zoom using vector data display. Heatmap. Heatmaps refer to the use of color gradient or opacity overlays to display data density. The advantage of heat maps is the data reduction in the aggregating algorithm. To determine the color/opacity of a data set at a location the data is first aggregated by either a polygon or a grid cell. The sum of the data in a given grid cell is then applied to the color gradient dot for that cell. If heatmaps are rendered client side they have good performance only up to the latency limits of service side queries, internet bandwidth, and local rendering. Fig 6 - Example of heatmap canvas over Bing Maps rendered client side. Grid Pyramids – Server side gridding. Hybrid server side gridding offers significant performance advantages when coupled with pre- processed grid cells. One technique of gridding processes a SQL data resource into a quadkey structure. Each grid cell is identified by its unique quadkey and contains the data aggregate at that grid cell. A grid quadkey sort by length identifies all of the grid aggregates at a specific quadtree level. This allows the client to efficiently download the grid data aggregates at each zoom level and render locally on the client in an html. Bing Maps view. Since all grid levels are precompiled, resolution of cells can be adjusted by Zoom Level. Pro: Efficient display of very large data sets at wide extents. Can be coupled with vector displays at higher zoom levels for event driven objects. Con: gridding is pre- processedreal time data cannot be displayedstorage and time limitations apply to queries that have large numbers of permutations. Fig 7 – Grid Pyramid screen shot of UI showing opacity heatmap of Botnet infected computers. Thematic. Thematic maps use spatial regions such as states or zipcodes to aggregate data into color coded polygons. Data is aggregated for each region and color coded to show value. A hierarchy of polygons allows zoom levels to switch to more detailed regions at closer zooms. An example hierarchy might be Country, State, County, Sales territory, Zipcode, Census Block. Pro: Large data resources are aggregated into meaningful geographic regions. Analysis is often easier using color ranges for symbolizing data variation. Con: Rendering client side is limited to a few hundred polygons. Very large data sets require pre- processing data aggregates by region. Fig 8 - thematic map displaying data aggregated over 2. DMA regions using a quantized percentile range. Future trends. Big Data visualization is an important topic as the web continues to generate massive amounts of data useful for analysis. There are a couple of technologies on the horizon that help visualization of very large data resources. A. Leverage of client side GPUHere is an example Web. GL http: //www. web- maps. Web. GLTest using Canvas. Layer. Data performance is good up to about 5. Brendan Kenny. Complex shapes need to be built up from triangle primitives. Tessellation rates for polygon generation approaches 1,0. Once tessellated the immediate mode graphic pipeline can navigate at up to 6. Sample code is available on github. This performance is achieved by leveraging the client GPU. Because immediate mode graphics is a powerful animation engine, time animations can be used to uncover data patterns and anomalies as well as making some really impressive dynamic maps like this Uber sample. Unfortunately all the upstream latency remains: collecting the data from storage and sending it across the wire. Since we’re talking about larger sets of data this latency is more pronounced. Once data initialization finishes, client side performance is amazing. Just don’t go back to the server for new data very often. Pro: Good client side navigation performance up to about 5. Con: requires a webgl enabled browserrequires GPU on the client hardwaresubject to latency issues of server query and internet streaming. Web. GL tessellation triangle primitives make display of polylines and polygons complex. Fig 9 – test web. GL 3. 0,0. 00 random generated points (requires Web. GL enabled browser – Firefox, Chrome, IE1. Note: IE1. 1 added Web. GL capability which is a big boost for the web. There are still some glitches, however, and gl. Leverage of server side GPUMap. D – Todd Mostak has developed a GPU based spatial query system called Map. D (Massively Parallel Database). Map. D is a new database in development at MIT, created by Todd Mostak. Map. D stands for “massively parallel database.”The system uses graphics processing units (GPUs) to parallelize computations. Some statistical algorithms run 7. CPU- based systems like Map. Reduce. A Map. D server costs around $5,0. Map. D runs at between 1. SQL to query data. Mostak intends to take the system open source sometime in the next year. Bing Test: http: //onterrawms. Bing Test shows an example of tweet points over Bing Maps and illustrates the performance boost from the Map. D query engine. Each zoom or pan results in a Get. Map request to the Map. D engine that queries millions of tweet point records (8. Oct 1. 9 – Oct 3. Bing Map. 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