Classifications

• • G—PHYSICS
  • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
  • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
  • G09B29/00—Maps; Plans; Charts; Diagrams, e.g. route diagram
  • G09B29/003—Maps
  • G09B29/006—Representation of non-cartographic information on maps, e.g. population distribution, wind direction, radiation levels, air and sea routes
  • G09B29/007—Representation of non-cartographic information on maps, e.g. population distribution, wind direction, radiation levels, air and sea routes using computer methods
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
  • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
  • G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
  • G01C21/3863—Structures of map data
  • G01C21/387—Organisation of map data, e.g. version management or database structures
  • G01C21/3878—Hierarchical structures, e.g. layering
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
  • G06F16/29—Geographical information databases
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
  • G06F16/51—Indexing; Data structures therefor; Storage structures
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
  • G06F16/53—Querying
  • G06F16/532—Query formulation, e.g. graphical querying

Definitions

• the invention relates to a method for arranging object data in electronic cards with the preamble features of patent claim 1.
• Methods are generally known for arranging object data in electronic cards.
• a data area is provided with coordinate data of a spatial area.
• Object data for different objects are assigned to the coordinate data.
• the object data is for example a photo or a
• Text description of a specific location or attraction in a specific location is reduced.
• clustering is performed.
• the information or object data for an object are not displayed on the map itself but represented by a small symbol which is mapped on the map to the corresponding location of the assigned coordinate data.
• the symbol for example in the case of a display on a computer by clicking with a cursor arrow, the information in the form of the object data for this object is then displayed in a separate window, which is faded over a map section.
• Map area the reduction of the object data to clustered symbols allows almost seamless scrolling due to the reduced amount of data.
• LbS location-based service solutions
• the object of the invention is to provide a method for
• Arranging object data to be proposed in electronic cards which allows improved performance in displaying an electronic card on a device, in particular when an enlarged section of a larger data area by scrolling within the larger
• a method for arranging object data in electronic maps in which a data area with coordinate data of a spatial area is provided, object data for objects are assigned to the coordinate data and clustering for reducing the amount of data is carried out, wherein in the clustering different, spatially stand-alone objects are grouped together to form a cluster object.
• such a method is preferred in which the clustering is carried out such that in each case two mutually adjacent objects of a cluster lie within a predetermined distance value of the mutually adjacent objects.
• such a method is preferred in which different distance values for the clustering are specified for different spatial area sections.
• such a method is preferred in which a plurality of data stocks or copies of a data area with different clusters are provided on the basis of a mutually different distance value and a map to be displayed with different area sections is assembled from respectively corresponding sections of the corresponding data records.
• such a method is preferred in which the objects are sorted one after the other according to their distance values before clustering.
• such a method is preferred in which the objects are sorted according to the criterion of the minimum distances over the totality of all distances to one another.
• such a method is preferred in which the objects are arranged in a structured manner along a path.
• such a method is preferred in which the objects are arranged structured in paths of a tree structure.
• such a method is preferred in which, in the manner of zooming, the distance values, in particular maximum distance values, of mutually adjacent objects are changed to form clusters.
• such a method is preferred in which the clustering is carried out along the paths.
• such a method is preferred in which a distance-dependent sorting of the objects or objects jektrackingen between each other and / or a distance-dependent formation of clusters when adding a new object along one or more of the existing paths is performed.
• such a method is preferred in which, when adding a new object, existing paths are checked and if necessary deleted and / or new paths are added.
• a method for arranging object data in electronic maps in which a data area is provided with coordinate data of a spatial area, object data for objects are assigned to the coordinate data, and clustering for reducing the amount of data is carried out. spatially independent objects are combined to form at least one cluster object.
• the object data may be, for example, a photograph of a building that forms the object.
• a recorded or over a loudspeaker to be played tone sequence or an informative text can e.g. are provided as object data of an object and assigned to the corresponding coordinate data of the object within the data area.
• the data area of an electronic card is a spatially much larger area than the area which is to be displayed on a screen of a display device as a map to be displayed or a map section to be displayed.
• a large map display scale can display only a single icon as a cluster object only in another scale to be displayed with higher resolution than three individual objects or symbols is resolved to corresponding objects.
• Real-time clustering is thus possible in clustering with one or more cluster intensities on both undistorted and distorted maps. You can do this in a simple way with zoom-dependent clustering.
• automatic clustering can be implemented, which depends on the maximum number of available units or objects. A threshold can always be kept so that the number of clusters and objects does not exceed a certain desired number.
• FIG. 1 shows a data area of a spatial area in which object data associated with objects corresponding to coordinate data of the data area and distance information between each of the objects;
• Fig. 2 in different figures, starting from a
• FIG. 3 shows, based on a multiplicity of individual objects in a data area, clustering of different degrees for a central area of a map and an outer area of a map to be displayed, as well as combined representations with differently clustered central inner area and peripheral outer area of the map;
• FIG. 4 shows a representation with a distorted outside area and in comparison with an embodiment according to FIG.
• FIG. 5 shows a tree structure for illustrating a data structuring for enabling a particularly rapid adjustment of a clustering intensity.
• a plurality of objects Pi, Pl-P3, P5-P12 are arranged in a data area D with coordinate data of a spatial area.
• the arrangement of the objects Pl - P12 takes place in that corresponding object data are assigned to the coordinate data of the underlying data area D.
• the same data area D is shown with the same objects P1-P3, P5-P12.
• the shortest distances or distance values 13, 14 to all other objects P 2, P 3, P 5-P 12 in the data area D are determined.
• the respectively shortest distances to all objects Pl, P3, P5-P12 of the data area D are also determined by the next adjacent object P2.
• a path is finally determined which connects all objects Pl - P3, P5 - P12. Shown are the resulting minimum distances or distance values 13 - 20, 21 *, 22 * in the middle figure in Fig.l by corresponding connecting lines.
• minimum distance values 13-20, 21 *, 22 * are understood to mean values which take into account a weighting over the entirety of all possible distances between in each case two of the plurality of objects Pi while reducing e.g. to optimize the overall path length.
• the determined distance values 13 - 20, 21 *, 22 * are entered with their object pairs in a list sorted according to the numerical value of the distances.
• another fourth object P4 is subsequently added to the plurality of objects P1-P3, P5-P12.
• the smallest distances to all other objects Pl - P3, P5 - P12 are again determined.
• the originally shortest distance value 22 * or path between the second and the sixth object P2, P6 is no longer optimal considering the entirety of the shortest possible connections. Accordingly, the original pair of links from these two objects P2, P6 or the path or shortest distance value 22 * between them is deleted from the previously created list of shortest distances. Instead, a new shortest distance value 22 or a corresponding path between the newly inserted object P4 and the original sixth object P6 is set.
• FIG. 2 shows six data areas D, each with the same plurality of objects Pi.
• the non-interconnected objects Pi are sketched at the corresponding positions in a map.
• additional corresponding connections Vi are shown, which reproduce the respectively most suitable shortest distances between two objects, as they are also entered in the list.
• the path pulling through the plurality of objects Pi will have individual branches if a linear guide proves unsuitable for the optimum minimum distances of the total of all distances, resulting in a bounded tree structure.
• a scale is displayed which shows the clustering intensity selected for this display.
• the clustering of individual objects Pi is selected as a function of the respective distance values Vi of two mutually adjacent objects Pi.
• a very low clustering density or clustering intensity is shown, in which only very closely spaced objects Pi are detected. Accordingly, only three clusters Cl, C2, C3 are formed.
• the clusters are thereby formed with orientation on the formed compounds Vi or paths, which were previously formed according to the criterion of the shortest possible distances in order to allow a particularly rapid adjustment of the clustering density.
• each of the clusters forms its own cluster object with the combined objects, wherein the clusters C 1 - C 3 are each displayed as a single cluster object on a map that is actually to be displayed, in particular as an icon.
• the number of clusters Cl - C8 formed from a plurality of individual objects Pi first increases and then finally to a single cluster comprising all objects Pi Cl again. If each of the individual objects Pi in the illustration on the top left was regarded as a separate cluster with the lowest possible clustering intensity, the number of clusters would increase with increasing clustering intensity
• FIG. 3 again shows a data area D on the top left, which is to be displayed completely on a display device as a map and optionally forms a subsection of a larger data area.
• a plurality of objects Pi is arranged in the data area.
• two different representations of this data area D are shown with different clustering intensity, the clustering intensity in turn being dependent on the respectively smallest distance values A between any two mutually adjacent objects Pi.
• a mean clustering intensity corresponding to the upper left representation is selected for a central area DC as a first area section.
• the outer area or the periphery DA of the data area D has very little or no
• Clustering on Such a representation makes it possible to display a map with low cluster object resolution in the central map area, which leads to a reduced stress on the concentration of a viewer of the map to be displayed, who is scrolling over a larger data area.
• the right-hand lower illustration shows a central region DC again with a moderate clustering and two clusters C3, C4, while the periphery DA has a much stronger clustering corresponding to the middle representation of the upper row. Accordingly, there are only a few large clusters C1, C2 in the periphery DA of the data area D.
• Such a representation allows a particularly effective and seamless scrolling through a larger data area, wherein the viewer of the displayed and displayed map are displayed in each case for the central area DC cluster objects with a larger display resolution, ie with a lower clustering intensity.
• the outer area, which is formed by the periphery DA, is usually of lesser interest to the observer during a scrolling process and is paid less attention, so that a high clustering intensity is acceptable for the periphery.
• FIG. 4 shows, starting from the illustration in FIG. 3, bottom left, a situation with a central area DC with a medium clustering intensity and individual cluster objects C3, C4, while in the exterior area individual objects P1, P2 are shown without any clusters.
• a spatially distorted representation is used in which the central area DC is displayed to scale, while toward the outer edges of the display area towards an increasingly greater distortion or increase in the representation scale is made.
• far away from the central area DC lying objects Pl, P2 are thereby imaged close to the central area DC.
• an increasingly higher clustering intensity towards the outer circumference of the periphery DA * is advantageous, but in the present case this is not outlined merely for reasons of representation.
• FIG. 5 shows a data structure for simultaneously displaying and adjusting various clustering intensities above embodiments and other embodiments in a spatial memory map with two zones for a central area DC center and for a periphery DA of the area to be displayed. In addition, two different cluster intensities are taken into account.
• each of the individual objects P1-P12 has its own clustering or order object 1, 2,... Or... 12 assigned to a first clustering level.
• first order objects 1 - 12 are formed, which are finally assigned in each case to a minimum distance value with the effective value 0 for a connection to itself.
• the further illustrated order objects 13 - 23 correspond to recursively formed shortest distances, taking into account the totality of the objects Pl - P12 and the totality of the distance values 1 - 23, so that the terms distance value and order object are interchangeable.
• the individual ones of these objects 1 - 23 are in a list corresponding to the criterion of the shortest effective distances for forming a path structure or a tree structure, for example. is constructed according to the comments on Fig. 2, ordered.
• clusters Cl * - C5 * formed corresponding to 5, or eight clusters Cl - C8 formed.
• eight groupings form as clusters C2, C3, C5 - C7, whereby five of the objects P4, P5, P8 - PlO alone each have their own cluster object with only themselves as a single object form.
• two cluster objects are created as the clusters C4, C8 with the objects P6, P7, and PlI, P12, respectively. det.
• These two clusters C4, C8 are connected by the order objects with the numerical values 15 and 16 as upper cluster elements or top level order objects.
• Another large cluster object is formed by the first cluster Cl with the objects Pl - P3, this first
• Cluster Cl is assigned a first order object 13 for connecting the first objects Pl, P2 and a second order object 14 for connecting the third object P3 to the first order object 13 or above with the first object Pl.
• the first cluster Cl would accordingly be formed by the first three objects Pl, P2, P3, which are interconnected by the order objects or shortest distance values 13, 14.
• the fourth cluster C4 would be formed in Fig.l by the centrally located points P6, P7 with the order object 15 between them.
• the eighth cluster C8 would be formed by the points PlI, P12 with the order object or distance value 16.
• clustering intensity is to be increased, this is equated to an increase in the underlying distance values A, so that it is no longer only the objects Pi that are clustered to the order objects, ie distance values 1-16, but, according to the exemplary embodiment illustrated, a clustering to for example, to the order object or distance value 19 is made.
• This has the effect that instead of 8 only 5 clusters Cl * - C5 * are formed as shown at the bottom left in Figure 5. While the first three and the last of the previously formed clusters Cl - C3, C8 remain effectively clustered unchanged, the remaining original clusters C4 - C7 are combined into a single new cluster C4 *.
• a tree structure can be discerned, which starts with an order object or largest minimizable distance value 23 and forms a subdivision of a data structure depending on the next lower-ranking order objects or smaller distances 22, 21, 20, 13 via path formations and branches.
• This allows a simple way simultaneous display and adjustment of different cluster intensities in a spatially stored map display. Without performing a complete new calculation of individual objects or their map coordinates and object relationships with one another in order to form new cluster objects, a clustering can be carried out or adjusted in a simple manner depending on the respectively desired maximum distance value or order object.
• the concept enables seamless and efficient scrolling of automatically clustered spatially arranged objects or information units.
• the clustering intensity can be adjusted separately and in real time for each desired area.
• the memory requirement for the necessary data structures is linear, the time required for the calculation to be performed only once in terms of the total number of objects is square.
• Each further addition of a new object is only proportional to the objects Pi present in the system.
• clustering can be visualized from maximum clustering intensity with only a single large cluster to minimal clustering even with large amounts of data in real time.
• it is advantageously possible to work with more than one clustering intensity or clustering stage in real time on the same data structure. If necessary, different clustering intensities are produced on different copies of the object data of the data area in order to be able to display map sections from the different copies for a map to be displayed with differently clustered sections.
• a suitable data structure for a real-time clustering is advantageously formed in a first step, in which the shortest distances between respectively two objects of the plurality of objects Pi are determined and stored.
• the newly added object is preferably connected to the object, which realizes the minimum distance to all previously existing objects. This procedure applies except for the first object, which is merely placed without forming and storing a connection pair.
• a second step for integrating a new object the distances of existing connection pairs or object pairs are checked.
• the existing connection is Binding between the other two objects first deleted and then replaced by a new connection, as illustrated by the transition of the middle to the lower view of FIG. 1.
• This procedure is performed until an end of the path or a branch has been reached, with ends causing the end of a path and branches initiating new branches of such a recursive tree respectively.
• the procedure is carried out so long, possibly also with newly created paths, until such recursive path reconstruction has processed or replaced all existing connection distances with possible new connection distances between the new object and the existing objects.
• a distance-ordered list of distance pairs or distance values which form the order objects or list on the right in FIG. 5, is returned. If appropriate, such a list formation can also be carried out already during the first step of the integration of a new object.
• connection pairs or distance values A are sorted by intervals in a list and stored via a control element managed in the form of a mechanically operable, electronically operable or virtually operable switch such that an order object or distance can be set proportional to the position of the operating element, as with reference to FIG. 5 or the various Cluste- intensities according to FIG is described.
• An advantageous algorithm or a corresponding procedure for a clustering preferably starts with a maximum resolution or minimal clustering intensity at which all objects Pi form their own cluster with only themselves as an object.
• the next order object or the next distance value A in the list ordered by distances is clustered as long as both objects of a corresponding pair of objects of the corresponding order object or of the corresponding connection of two objects determined by the distance are first theirs determine assigned next higher cluster.
• the entire tree structure of FIG. 5 is finally processed, wherein an increasingly larger number of individual clusters of multiple objects usually arises and ultimately decreases the number of individual clusters forming fewer clusters with an even larger number of individual objects per cluster.
• Forming a graded list and tree structure allows cluster realizations to be visualized, even with large amounts of data in real time, with clusters ranging from maximum clustering intensity to ultimately only a single large cluster, to a minimum clustering intensity at the end each individual object as a separate cluster, which then actually is not a real cluster, can be formed.
• such a procedure can be applied to a single and same data structure at several clustering stages.
• one or more intermediate layers can be created in the ordered list with the order objects.

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• Physics & Mathematics (AREA)
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• 2005
  • 2005-03-31 DE DE102005014761A patent/DE102005014761A1/de not_active Withdrawn
• 2006
  • 2006-03-15 CN CN2006800106387A patent/CN101151611B/zh not_active Expired - Fee Related
  • 2006-03-15 EP EP06725086A patent/EP1864233A1/de not_active Withdrawn
  • 2006-03-15 WO PCT/EP2006/060770 patent/WO2006103177A1/de active Application Filing
  • 2006-03-15 US US11/887,663 patent/US20080147660A1/en not_active Abandoned

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