1. Field
This invention generally relates to systems and methods for organizing, managing, and graphically displaying multiple logically related entities and, more specifically, in one embodiment, to systems and methods for the real time organization and display of clusters of tracked vehicles in connection with a geographical map.
2. Description of Related Art
As the use of electronic maps to display information has increased in popularity, so has the volume and complexity of information to be displayed. Accordingly, it has become more and more difficult to take in the wide range of data that may be overlaid and viewed on maps without the viewer becoming confused or overwhelmed by the display.
This problem is acutely expressed in the field of map-based or location-based services and took, where the ever increasingly detailed nature of the information to be displayed, together with the potentially limitless number of sources from which data is gathered, combine to make it extremely challenging to display such information on a map in a logical manner that is accessible and useful to viewers. For instance, the management of large numbers of people and equipment involves obtaining timely information about location, status, and potential alarm conditions. Management preferably wants to be able to observe when and where scheduled events have occurred and how schedules and status have changed since the previous observations, but doing so in prior systems was difficult if not impossible with so much data available. For example, in the field of fleet management, it is desirable to know the status of vehicles in a fleet, where the vehicles currently are, whether the vehicles are operating properly, etc. For fleets with hundreds or thousands of vehicles, such information quickly became overwhelming when accessed through a map-based interface.
Developers have attempted to facilitate the ease of use of such map-based displays by allowing viewers to zoom in and out of the maps at issue; however, this is often an ineffective solution since the problem of viewing an overwhelming amount of data is only amplified as the viewer zooms out to get a more general picture of the data. As a result, in order to make sense of the displayed data, the viewer's only choice is to zoom in so far on the map that important information is no longer displayed onscreen. Other systems simply operate too slowly to meet the demands of full time asset management.
The present embodiments overcome these and other deficiencies of the prior art by providing systems and methods for smart zooming that cluster geographically or spatially related information together to create useful overlays of data to facilitate the management of such information. In some embodiments, the information pertains to vehicles being tracked. In some embodiments, the systems and methods provide the real-time display of status and location information for a fleet of tracked vehicles. In some embodiments, the systems and methods permit the automatic clustering of assets based on a user's view of an underlying map, which change dynamically as a user zooms in or out of a map. In some embodiments, the assets are vehicles that are a part of a managed fleet. In some embodiments, the assets are logically arranged into clusters of like assets. In some embodiments, the clusters provide graphical indications of status or class information of their underlying assets.
In some embodiments, a method for displaying information pertaining to a plurality of geographically related assets is provided. The method includes receiving information pertaining to a plurality of assets; selecting the assets from the plurality of assets that are geographically related; forming a cluster comprising the selected assets; and providing a graphical user interface comprising a geographic map and the cluster. In some embodiments, the assets, are geographically related if the physical location of the assets are within a geographic area defined by a virtual bounding area. In some embodiments, the cluster is overlayed on the geographic map in the geographic area defined by the bounding area. In some embodiments, the cluster displays on the graphical user interface at least one item of information pertaining to the selected assets. In some embodiments, the assets are vehicles. The information pertaining to the plurality of assets is, in some embodiments, updated in real time. In some embodiments, at least one item of information is a chart displaying status information for the selected assets. In some embodiments, at least one item of information is a chart displaying the class information for the selected assets. In some embodiments, at least one item of information is a numerical indication of the number of selected assets. In some embodiments, the graphical user interface displays the virtual bounding area. In some embodiments, the method also includes adding the cluster to a new cluster if a user of the graphical user interface zooms out from the geographic map. In some embodiments, the method also includes splitting the cluster into a plurality of clusters if a user of the graphical user interface zooms in on the geographic map.
In some embodiments, a method for splitting a cluster of assets is provided, where each asset has pixel coordinates based on its geospatial location and in reference to a virtual area representing an end user's display. The method, in some embodiments, comprises identifying the coordinates of each asset; dividing the virtual area into slots; grouping the assets into the slots based on each assets' pixel coordinates; and generating new clusters, wherein each new cluster comprises at least one asset and comprises every asset which was grouped into the same slot.
In some embodiments, a method for clustering assets is provided. The method, in some embodiments, includes generating pixel coordinates for each asset based on the asset's geospatial location and in reference to a virtual area representing an end user's display; determining an icon shape with specific pixel dimensions for use in connection with each asset; querying a data structure with the icon shape for each asset; adding an asset as a new cluster in the data structure if the area defined by the pixel dimensions of the asset's icon shape, when centered over the asset's pixel coordinates, does not overlap with the area covered by another cluster in the data structure; and adding an asset to an existing cluster in the data structure if the area defined by the pixel dimensions of the asset's icon shape, when centered over the asset's pixel coordinates, overlaps with the area covered by the existing cluster.
For a more complete understanding of the present embodiments, the objects and advantages thereof, reference is now made to the following descriptions taken in connection with the accompanying drawings in which:
Corresponding reference characters indicate corresponding components throughout the several views of the drawings. Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions, sizing, and/or relative placement of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of various embodiments of the present invention. Also, common but well-understood elements that are useful or necessary in a commercially feasible embodiment are often not depicted in order to facilitate a less obstructed view of these various embodiments of the present invention. It will also be understood that the terms and expressions used herein have the ordinary meaning as is usually accorded to such terms and expressions by those skilled in the corresponding respective areas of inquiry and study except where other specific meanings have otherwise been set forth herein.
Further features and advantages of the present embodiments, as well as the structure and operation of the various embodiments, are described in detail below with reference to the accompanying
Referring now to
The information in the cluster 100 is, in some embodiments, updated in real time. For example, as a vehicle leaves or enters the bounding area 102, the numerical value 104 and charts 106 and 108 are updated accordingly. If the status of an asset changes, the outer chart 108 likewise is updated. Such real-time updates are configurable by the user of the system and may be dependant on the communication systems of the assets. For instance, a user of the system may only desire updates once every five minutes. Alternatively, an asset may be programmed to only provide its location and other status information once every thirty seconds, or only when its status or location changes. Those of ordinary skill in the art will therefore recognize the information displayed by the cluster 100 may, in some embodiments, be delayed based on these contingencies or preferences. Alternatively, the updating of information can be done, for example, when the map is resized.
In some embodiments, not all assets in a specific bounding area 102 are included in the cluster 100. For instance, the cluster 100 may only comprise assets of a certain type, such as only vehicles. In other embodiments, the cluster 100 may only comprise assets owned by a specific entity, such as a single rental car company. The specific assets that are included in a cluster 100 may be selected based on numerous attributes, such as the asset's status, class, physical attributes, type, ownership information, etc. In some embodiments (not shown), the bounding area 102 comprises more than one cluster. Each of clusters may comprise assets that, but for the preferences of the user specified to the system, would be included in the same cluster, or assets that, due to different attributes, belong in different clusters. For the sake of clarity, the following discussion only discusses a single duster 100 being within a single bounding area 102. The bounding area 102, as shown, is a square or rectangle, but other shapes, such as polygons, circles, triangles, or other regular or irregular shapes or any combination thereof, are used in other embodiments. Furthermore, in some embodiments, the bounding area 102 may have different colors or levels of opacity. By altering the alpha channel value (the opacity) and/or the color of the bounding area 102, additional information can be displayed pertaining to each bounding area 102 displayed on a map, such as which bounding area 102 contains more assets.
Referring now to
Referring now to
Referring now to
Referring now to
In some embodiments, the structure of assets and clusters can be thought of as follows:
(a) Asset
(b) Point
(c) Zone
(d) Area
(e) Regions
The manner in which the system clusters assets (or clusters) and splits clusters as discussed above is now described in more detail, according to an embodiment. Referring to
Referring now to
In some embodiments, the method described with reference to
In some embodiments, clusters are split if they are either horizontally or vertically larger than a defined variable M (cluster maximum size). This variable, for example, is based on the resolution of the user's screen and on the size of typically icons used with the system. On a standard screen using icons of 32 pixels wide, M is, in some embodiments, set at 100 pixels. Those of ordinary skill in the art will recognize that other values for M may be optimal, depending on the user's screen's resolution and size, the size of the icons used in connection with the system, or other variables.
If a cluster is to be split, then the cluster is split both horizontally and vertically. Vertical and horizontal maximum pixel values for the new clusters are, in some embodiments, based on the following formulas:
Ysplit=M−2(ICONwidth)
Xsplit=Ysplit/2+Ysplit
Where Ysplit is the maximum horizontal pixel value and Xsplit is the maximum vertical pixel value, and ICONwidth is the width, in pixels, of the asset or cluster icons. The foregoing formulas take into account the dimensions of the icons to ensure clusters will not overlap on a user's screen. Based on the foregoing, where M is set to 100 pixels and the icons are 32 pixels, each new cluster must be less than 54 pixels wide, and must be less than 36 pixels tall.
Referring now to
Referring now to
Referring now to
Referring now to
Location, status, alarm, class, speed, and other types of pertinent information pertaining to a devices' asset may be provided by the devices 1602a-c to a central processing system 1604. In some embodiments, the devices 1602a-e communicate such information in real time. In other embodiments, such information is transmitted periodically, at random intervals, or on an alarm condition. Such transmissions are made, in some embodiments, via a wireless or satellite based communication system and/or over the internet or similar wide area network 1606, to the central processing system 1604.
The central processing system 1604, in some embodiments, comprises one or more web servers 1608 in communication with one or more databases 1610. The web servers 1608 may, in some embodiments, have high performance or computer clustering capabilities. The central processing system 1604, operates, in some embodiments, as follows:
In some embodiments, the central processing system 1604 utilizes distributed or cloud computing capabilities and/or technology. In some embodiments, the central processing system 1604 shares computing responsibilities with a requesting client 1612a-c. In other embodiments, the central processing system 1604 is responsible for all or substantially all of the computing responsibilities. A benefit of the foregoing arrangements, in some embodiments, is that the amount of data that needs to be transferred from the web server 1608 to the client 1612a-c is very small. In some embodiments, little, if any, of the underlying asset information is transferred to clients 1612a-c. In these embodiments, merely the high-level cluster information showing the aggregated asset information (e.g. charts, numerical value, and/or bounding area information, such as that discussed with reference to
A client 1612a-c may be, for example, a personal computer or a smart phone. In other embodiments, a client 1612a-c may itself be a server or central computer which is configured to transmit the information received by the central processing system 1640 to end-user clients (not shown). In some embodiments, the clients 1612a-c comprise a graphical user interface displayed in a browser window of a browser application. Such an arrangement permits an end user of a client 1612a-c to easily view the map, geographic information, cluster information, and the underlying asset data provided by the central processing system 1604. Thus, the system and its smart zooming capabilities can be utilized by any user with a client computing device capable of running a web browser and accessing a wide area network, such as the Internet.
In some embodiments, the graphical user interface is implemented using HTML, JavaScript, CSS, BON, and/or XML programming. Such programming may be AJAX compliant. In some embodiments, a dynamic HTML page or XML content is created by the central processing system 1604 in response to a request by a client 1612a-c. Such interaction, in some embodiments, proceeds by the user of a client 1612a-c making XML API calls and/or Java Applet calls to the web server 1608 of the central processing system 1604. Using the techniques previously discussed, the central processing system 1604 generates dusters, asset information, and or other information requested by the user, and provides such content, along with the corresponding map data, to the client 1612a-c. The graphical user interface of the browser window thereafter displays such content for viewing by a user.
The smart zoom system and its graphical user interface can be implemented using technologies other than those described. For instance, in one embodiment, the graphical user interface is implemented as an Adobe Flash object. In some embodiments, where the graphical user interface is implemented using Flash, the graphical user interface is embedded in an HTML page and executed by a Flash compatible plug-in for the browser application. The Flash object stores data files and/or communicates with the central processing system receive updated map, cluster, and asset information. In other embodiments, technologies such as Java, Java Applets, Synchronized Multimedia Integration Language (SMIL), or Microsoft Silverlight are used to implement the graphical user interface and to interact with the central processing system 1604. In other embodiments, the graphical user interface is executed by a standalone player external from the browser application or other specialized program used to access the central processing system 1604.
As is apparent, the described smart zoom clustering methods and systems allow diverse user tools and interfaces to permit any number of end user clients to visualize hundreds or thousands of assets and their real time location and status. By utilizing the forgoing smart zoom systems and methods, a vehicle fleet or other asset manager can quickly and easily determine the status of hundred of assets, identify problems with the assets, redeploy assets as needed, and make other managerial decisions in a manner not possible using other methods and systems.
A further embodiment is computer readable code or program instructions on one or more computer readable mediums capable of carrying out processes discussed above. A computer readable medium is any data storage device that is capable of storing data, or capable of permitting stored data to be read by a computer system. Examples include hard disk drives (HDDs), flash memory cards, such as CF cards, SD cards, MS cards, and xD cards, network attached storage (NAS), read-only memory (ROM), random-access memory (RAM), CD-ROMs, CD-Rs, CD-RWs, DVDs, DVD-Rs, DVD-RWs, holographic storage mediums, magnetic tapes and other optical and non-optical data storage devices. The computer readable medium can also be in distributed fashion over multiple computer systems or devices which are coupled or otherwise networked together and capable of presenting a single view to a user of the medium.
Yet another embodiment is a computer system or similar device configured to access computer readable code or program instructions from a computer readable medium and to execute program instructions using one or more CPUs to carry out embodiments as described. Such computer system can be, but is not limited to, a typical personal computer, microcomputers, a handheld device such as a cell phone, PDA, BlackBerry, or a more advanced system such as a computer cluster, distributed computer system, server accessed over wired or wireless devices, a mainframe, or a supercomputer. In another embodiment, the server(s) of the system are also stored in and accessed from the computer readable medium. In other embodiments, they are implemented using hardware.
In some embodiments, some or all of the content stored in the computer readable medium is transmitted via a similar network. In other embodiments, the central processing system generates signals or instructions based on the results of the program instructions and/or the contents of the computer readable medium.
In other embodiments, the foregoing systems and methods are applicable to environments other than those in two dimensions. For instance, three dimensional clusters, maps, bounding areas, and assets can be implemented using the same techniques and methods discussed above.
The invention has been described herein using specific embodiments for the purposes of illustration only. It will be readily apparent to one of ordinary skill in the art, however, that the principles of the invention can be embodied in other ways. Therefore, the invention should not be regarded as being limited in scope to the specific embodiments disclosed herein.
This application is a continuation application of U.S. patent application Ser. No. 12/882,930, filed Sep. 15, 2010 and entitled “Real Time Map Rendering with Data Clustering and Expansion and Overlay”, which is a continuation application of International Patent Application No. PCT/US2010/45630, filed Aug. 16, 2010 and entitled “Real Time Map Rendering with Data Clustering and Expansion and Overlay”, which claims priority to U.S. Provisional Patent Application Ser. No. 61/274,221, filed Aug. 14, 2009 and entitled “Real Time Map Rendering with Data Clustering and Expansion and Overlay,” the disclosures of the foregoing applications are incorporated herein by reference to the extent they are not inconsistent with the disclosure herein.
Number | Name | Date | Kind |
---|---|---|---|
5003317 | Gray et al. | Mar 1991 | A |
5638523 | Mullet et al. | Jun 1997 | A |
5808907 | Shetty et al. | Sep 1998 | A |
5904727 | Prabhakaran | May 1999 | A |
6025843 | Sklar | Feb 2000 | A |
6075530 | Lucas et al. | Jun 2000 | A |
6097998 | Lancki | Aug 2000 | A |
6112015 | Planas et al. | Aug 2000 | A |
6144920 | Mikame | Nov 2000 | A |
6216134 | Heckerman | Apr 2001 | B1 |
6252605 | Beesley et al. | Jun 2001 | B1 |
6308120 | Good | Oct 2001 | B1 |
6339745 | Novik | Jan 2002 | B1 |
6477452 | Good | Nov 2002 | B2 |
6556899 | Pachet | Apr 2003 | B1 |
6609061 | MacPhail | Aug 2003 | B2 |
6609064 | Dean | Aug 2003 | B1 |
6611755 | Coffee et al. | Aug 2003 | B1 |
6718263 | Glass | Apr 2004 | B1 |
6879910 | Shike et al. | Apr 2005 | B2 |
7143100 | Carlson et al. | Nov 2006 | B2 |
7158136 | Gannon | Jan 2007 | B2 |
7174243 | Lightner et al. | Feb 2007 | B1 |
7246009 | Hamblen | Jul 2007 | B2 |
7323982 | Staton et al. | Jan 2008 | B2 |
7395140 | Christie et al. | Jul 2008 | B2 |
7499925 | Moore | Mar 2009 | B2 |
7587411 | De Vorchik | Sep 2009 | B2 |
7743346 | Kyle | Jun 2010 | B2 |
7756615 | Barfoot et al. | Jul 2010 | B2 |
7828655 | Uhlir et al. | Nov 2010 | B2 |
7913179 | Sheha et al. | Mar 2011 | B2 |
7913188 | Krenz et al. | Mar 2011 | B1 |
8200376 | Mattingly et al. | Jun 2012 | B2 |
20010041566 | Xanthos | Nov 2001 | A1 |
20020022984 | Daniel | Feb 2002 | A1 |
20020059075 | Schick | May 2002 | A1 |
20020077750 | McDonald et al. | Jun 2002 | A1 |
20020111715 | Richard | Aug 2002 | A1 |
20030055666 | Roddy | Mar 2003 | A1 |
20030149526 | Zhou | Aug 2003 | A1 |
20030158635 | Pillar | Aug 2003 | A1 |
20040039504 | Coffee et al. | Feb 2004 | A1 |
20040073468 | Vyas | Apr 2004 | A1 |
20040077347 | Lauber et al. | Apr 2004 | A1 |
20040090950 | Lauber et al. | May 2004 | A1 |
20040254698 | Hubbard et al. | Dec 2004 | A1 |
20050080520 | Kline et al. | Apr 2005 | A1 |
20050090978 | Bathory et al. | Apr 2005 | A1 |
20050143909 | Orwant | Jun 2005 | A1 |
20050171835 | Mook | Aug 2005 | A1 |
20050195096 | Ward et al. | Sep 2005 | A1 |
20050222933 | Wesby | Oct 2005 | A1 |
20060074553 | Foo | Apr 2006 | A1 |
20060099959 | Staton et al. | May 2006 | A1 |
20060100777 | Staton et al. | May 2006 | A1 |
20060129691 | Coffee et al. | Jun 2006 | A1 |
20060187026 | Kochis | Aug 2006 | A1 |
20060190280 | Hoebel | Aug 2006 | A1 |
20060212327 | Norman | Sep 2006 | A1 |
20060244587 | Humphries | Nov 2006 | A1 |
20060276204 | Simpson | Dec 2006 | A1 |
20060287783 | Walker | Dec 2006 | A1 |
20070173993 | Nielsen et al. | Jul 2007 | A1 |
20080014908 | Vasant | Jan 2008 | A1 |
20080036778 | Sheha et al. | Feb 2008 | A1 |
20080045234 | Reed | Feb 2008 | A1 |
20080052142 | Bailey et al. | Feb 2008 | A1 |
20080071428 | Kim | Mar 2008 | A1 |
20080097731 | Lanes et al. | Apr 2008 | A1 |
20080121690 | Carani | May 2008 | A1 |
20080125964 | Carani | May 2008 | A1 |
20080174485 | Carani | Jul 2008 | A1 |
20080252487 | McClellan | Oct 2008 | A1 |
20080255722 | McClellan | Oct 2008 | A1 |
20080258890 | Follmer | Oct 2008 | A1 |
20080261565 | Kunz | Oct 2008 | A1 |
20080294690 | McClellan | Nov 2008 | A1 |
20080318597 | Berns | Dec 2008 | A1 |
20090003657 | Deardorff | Jan 2009 | A1 |
20090051510 | Follmer | Feb 2009 | A1 |
20090073034 | Lin | Mar 2009 | A1 |
20090077221 | Eisenstadt | Mar 2009 | A1 |
20090131012 | Ashley, Jr. | May 2009 | A1 |
20090132163 | Ashley, Jr. | May 2009 | A1 |
20090137255 | Ashley, Jr. | May 2009 | A1 |
20090138336 | Ashley, Jr. | May 2009 | A1 |
20090292464 | Fuchs | Nov 2009 | A1 |
20090326991 | Wei et al. | Dec 2009 | A1 |
20100023162 | Gresak | Jan 2010 | A1 |
20100076675 | Barth et al. | Mar 2010 | A1 |
20100076968 | Boyns | Mar 2010 | A1 |
20100115462 | Spencer | May 2010 | A1 |
20100153005 | Cerecke et al. | Jun 2010 | A1 |
20100205022 | Brown | Aug 2010 | A1 |
20100207751 | Follmer | Aug 2010 | A1 |
20100211340 | Lowenthal et al. | Aug 2010 | A1 |
20100274479 | Sheha et al. | Oct 2010 | A1 |
20100281381 | Meaney et al. | Nov 2010 | A1 |
20110016199 | De Carlo et al. | Jan 2011 | A1 |
20110016514 | De Carlo et al. | Jan 2011 | A1 |
20110040440 | de Oliveira et al. | Feb 2011 | A1 |
20110093306 | Nielsen et al. | Apr 2011 | A1 |
20110238457 | Mason et al. | Sep 2011 | A1 |
20110288762 | Kuznetsov | Nov 2011 | A1 |
20110289019 | Radloff et al. | Nov 2011 | A1 |
20120072244 | Collins et al. | Mar 2012 | A1 |
20120101855 | Collins et al. | Apr 2012 | A1 |
20120179361 | Mineta et al. | Jul 2012 | A1 |
Number | Date | Country |
---|---|---|
2607465 | Apr 2008 | CA |
WO 0022595 | Apr 2000 | WO |
WO 02075667 | Sep 2002 | WO |
WO 2008034097 | Mar 2008 | WO |
WO 2008110962 | Sep 2008 | WO |
WO 2011116330 | Sep 2011 | WO |
Entry |
---|
A Cost Effective Real-Time Tracking System Prototype Using Integrated GPS/GPRS Module; El-Medany, W.; Al-Omary, A.; Al-Hakim, R.; Al-Irhayim, S.; Nusaif, M.; Wireless and Mobile Communications (ICWMC), 2010 6th International Conference on Digital Object Identifier: 10.11. 09/ICWMC.201 0.1 04; Publication Year: 2010 , pp. 521-525. |
A framework algorithm for a real-world variant of the vehicle routing problem; Vu Pham; Tien Dinh; Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on; Digital Object Identifier: 10.1109/IEEM.2011.6118237. Publication Year: 2011 , pp. 1859-1863. |
Automatic bus routing and passenger geocoding with a geographic information system; Yue-Hong Chou; Vehicle Navigation and Information Systems Conference, 1995. Proceedings. In conjunction with the Pacific Rim TransTech Conference. 6th International VNIS. A Ride into the Future; Digital Object Identifier: 10.11 09IVNIS.1995.518861. |
Barcelo et al., “Vehicle Routing and Scheduling Models, Simulation and City Logistics”, Dept of Statistics and Operations Research, Universitat Politecnica de Catalunya, pp. 1-29 (accessed Aug. 22, 2011). |
Estimating positions and paths of moving objects; Beard, K.; Palancioglu, H.M.; Temporal Representation and Reasoning, 2000. TIME 2000. Proceedings. Seventh International Workshop on; Digital Object Identifier: 10.11 09/TIME.2000.856597 Publication Year: 2000 , pp. 155-162. |
International Preliminary Report on Patentability issued in application No. PCT/US2010/045630 on Feb. 23, 2012. |
International Search Report and Written Opinion, International Application No. PCT/US2010/045630, dated Mar. 30, 2011. |
MacLean et al., “Real-time Bus Information on Mobile Devices”, Department of Electrical Engineering, University of Washington, pp. 1-6 (accessed Aug. 22, 2011). |
Marine fleet allocation using data mining techniques; Ammar, M.H.; Ben Hafssia, S.; Masmoudi, Y.; Chabchoub, H. Logistics (LOGISTIQUA), 2011 4th International Conference on; Digital Object Identifier: 10.11 09/LOGISTIQUA.2011.5939394 Publication Year: 2011 , pp. 1-5. |
Office Action issued in New Zealand application No. 597951 on Nov. 8, 2012. |
Prognostics, from the need to reality-from the fleet users and PHM system designer/developers perspectives; Hess, A. Aerospace Conference Proceedings, 2002. IEEE; vol. 6; Digital Object Identifier: 10.11 09/AERO.2002.1 036118 Publication Year: 2002 , pp. 6-2791-6-2797 vol. 6. |
Real-time tracking management system using GPS, GPRS and Google earth; Chadil, N.; Russameesawang, A.; Keeratiwintakorn, P.; Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2008. ECTI-CON 2008. 5th Inter. Conf. on;vol. 1; Digital Object Id: 10.11 09/ECTICON.2008.4600454; Pub.yr. 2008 , pp. 393-396. |
Shepard, “Multidimensional Scaling, Tree-Fitting, and Clustering”, Science, New Series, 210(4468):390-398 (Oct. 24, 1980). |
Stodolsky et al., “Technology Options to Reduce Truck Idling”, Argonne National Laboratory, Transportation Technology R&D Center, University of Chicago, 16 pages, Mar. 15, 2001. |
UKM campus bus monitoring system using RFID and GIS; Mustapha, A.M.; Hannan, M.A.; Hussain, A.; Basri, H.; Signal Processing and Its Applications (CSPA), 2010 6th International Colloquium on; Digital Object Identifier: 10.11 09/CSPA.201 0.5545246; Publication Year: 2010, pp. 1-5. |
Van De Peer et al., “TREECON: a Software Package for the Construction and Drawing of Evolutionary Trees”, Computer Applications in the Biosciences, IRL Press at Oxford University Press, 9(2):177-182 (1993). |
Zhang et al., “BIRCH: An Efficient Data Clustering Method for Very Large Databases”, Sigmod, pp. 103-114 (1996). |
Wang, “Research on cartographic visualization for statistical data”, China Master Dissertations Full-text database, vol. 6 (Jun. 15, 2008). |
Office Action issued in Chinese application No. 201080036125.X on Jun. 3, 2014. |
Notice of the Third Office Action for corresponding Chinese Application No. 201080036125.X, mailed Jan. 12, 2015, in 11 pages. |
Extended Supplementary Search Report for Application No. 11776666.7-1955 / 2663930 dated Nov. 5, 2014, in 6 pages. |
Extended European Search Report for Application No. 10754608.7-1958 /2465024 PCT/US2010/045630, dated Dec. 23, 2014, in 6 pages. |
Number | Date | Country | |
---|---|---|---|
20150112741 A1 | Apr 2015 | US |
Number | Date | Country | |
---|---|---|---|
61274221 | Aug 2009 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 12882930 | Sep 2010 | US |
Child | 14285112 | US | |
Parent | PCT/US2010/045630 | Aug 2010 | US |
Child | 12882930 | US |