Patent Publication Number: US-8527519-B2

Title: Query system for a hybrid voxel and feature database

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 12/707,204 filed Feb. 17, 2010, now issued as U.S. Pat. No. 8,176,053 on May 8, 2012, and incorporated herein by reference in its entirety. 
    
    
     BACKGROUND 
     The present disclosure relates to the field of hybrid databases, and, more particularly, to a query system for a geospatial hybrid database that stores feature characteristics and volumetric attributes. 
     A geographic information system (GIS), or geographical information system captures, stores, analyzes, manages, and presents data that are linked to location. GISs represent a merging of cartography and database technologies. GIS databases record information using raster and vector based methodologies. Traditional GIS databases maintain records and record specific attributes in a vector based manner. That is, a set of discrete records for GIS objects are maintained. Attributes for these records are indexed against these discrete objects, in accordance with standard database techniques. For example, records of a traditional GIS database are indexed and normalized, often in third normal form (3NF). 
     Conventional GIS systems can store information in a raster and a vector based manner. In these systems, raw non-indexed visual data (e.g., raster based images) are typically maintained in a GIS as binary large objects (BLOB). Each BLOB generally represents a unique intelligence product that has been received. Analytics are performed against each BLOB. For example, a satellite image map can be converted to a vector structure by identifying adjacent cells with similar characteristics such as color intensity, generating vector regions for these cells, then converting regions into point, linear, or arial features. It is generally believed that there is a fundamental incompatibility between raster and vector based storage methodologies, which requires a GIS to be able to convert data from one structure to another. Specifically, data that is to be indexed and searched based on semantics is generally converted from a raster based storage format to a vector based one. 
     Conventional wisdom regarding vector based storage formats is that they require significant less overall storage space, are simpler to update and maintain than raster based storage, and that vector based storage allows for more analysis capability than raster based storage mechanisms. It is believed, as will be described herein, conventional wisdom assumptions regarding vector based storage benefits versus raster based storage are mistaken. 
     BRIEF SUMMARY 
     The disclosure is for a hybrid database that stores some geospatial information in voxel records (a raster based storage format) and other geospatial information in a feature records (a vector based storage format). Indexes are maintained between the voxel records and the feature records. Each feature is associated with a volumetric envelope formed from one or more primitive shapes that together approximate an output volumetric boundary of the envelope-enclosed feature. In the hybrid database, higher order attributes (which can be referred to as feature-level semantics) unique to the feature as a whole are stored in vector format in the feature records. Lower order attributes (which can be referred to as voxel-level semantics), which are not uniformly true for the envelope-enclosed feature, are stored in the voxel database in raster format. Stated differently, attributes pertaining to the entire geospatial envelope (a unique feature or real-world object), are stored in the feature records. Attributes pertaining to subcomponents of the geospatial envelope or to specific points of the interior of the volumetric defined space are stored in the raster form in the voxel records. 
     The hybrid database can include a hybrid intake engine, which processes incoming information from a real-world space in accordance with internal rules. After processing, some portions of the incoming information will be stored in feature records while other portions will be stored in voxel records. A minimal amount of redundancy will exist in the hybrid database, as different attribute types map to different portions of the hybrid database (feature-level attributes mapping to specific features—volumetric space or voxel-level attributes mapping to specific voxels). The hybrid database can also include a hybrid query engine, which is able to respond to hybrid queries which have both feature-based and voxel-based elements. That is, portions of data are extracted from the feature database and other portions are extracted from the voxel records in response to a hybrid query. A fact that a hybrid query produces results from both a raster format space (voxel database) and a vector formatted space (feature database) can be transparent to an end-user issuing the hybrid query and receiving a hybrid query response. 
     In one aspect of the disclosure, a hybrid database can receive a hybrid query for an object having a real world analog. A feature of a feature database can be determined that corresponds to the object. Feature-level attribute values of the feature can be extracted from the feature database. A volumetric envelope forming an outer boundary of the feature in a volumetric storage space of the hybrid database can be determined. A set of uniquely indexed voxels can be determined. Each voxel can be a volumetric unit of the volumetric storage space that represents the volume contained by the volumetric envelope. Voxel-level semantic values can be extracted from the set of uniquely indexed voxels from a voxel database. Extracted voxel-level semantic values and extracted feature-level attribute values can be combined to generate a hybrid result. The hybrid result can be conveyed to a requestor from which the hybrid query was received. 
     In one aspect of the disclosure, sensors can capture raw data that geospatially corresponds to a real world volumetric space. The real-world volumetric space can be segmented into a set of regular volumetric units, where datum of the raw data is indexed against the regular volumetric units. Each regular volumetric unit of the real-world volumetric space can be mapped to a voxel in a storage volumetric space. Semantic content of the raw data can be bifurcated into voxel-level semantic content and feature-level semantic content. Voxel-level semantic content of the raw data can be stored in a voxel database of a hybrid database. The voxel-level semantic content can be contained in attributes of records having a unique voxel identifier. Feature-level semantic content of the raw data can be stored in a feature database of a hybrid database. The feature-level semantic content can be contained in attributes of records having a unique feature identifier. 
     One aspect of the disclosure is for a hybrid database for geospatial data. The hybrid database can include voxel records, feature records, and a hybrid query engine. The voxel records can have a unique voxel identifier. The voxel records can store information in a storage volumetric space comprising a plurality of voxels. The storage volumetric space can correspond to a real-world volumetric space that includes real-world volumetric units. A correspondence can exist between voxels and real-world volumetric units. The feature records can each represent a tangible object. Each feature record can comprises a unique feature identifier. Each feature of the feature records can have a three dimensional outer boundary defined by an envelope. The envelope can equate to a defined volume of the storage volumetric space. The envelope can be directly mapped to a plurality of voxels in the voxel records. The hybrid query engine can receive information hybrid queries conveyed to the hybrid database. It can query content stored in both the voxel records and feature records to generate a hybrid result for the hybrid queries. It can then provide the hybrid result to a requestor. An interface between the hybrid database and the requestor can be a unified interface that abstracts specifics that queries handled by the hybrid query engine are processed by two different types of records, which are the voxel records and the feature records. 
     Another aspect of the disclosure is for a database for storing probabilistic geospatially referenced information within an indexed tangible storage medium. The database can include voxel records, feature records, and shape records. The voxel records can be stored in a voxel table, where each of the records has a unique voxel identifier. Each voxel record can include voxel-level semantic attributes for the geometric space. Voxel-level semantic attributes can include appearance attributes, spectral signature attributes, and material composition attributes. The feature records can be stored in one or more feature tables. Each of the feature record can include feature-level semantic attributes, which include a unique feature identifier, a feature type, and a set of feature attributes. Feature attributes can include physical dimensions of a related feature, a geographic name of a location of the feature, and at least one functional usage attribute for the feature. Each feature associated with a unique feature identifier can correspond to a real world object. The shape records can be stored in a shape table. Each of the shape records can include a unique shape identifier, a shape type, a set of shape attributes, and a foreign key to a feature identifier. Types of shapes are primitive shapes that comprise a box, a cylinder, a sphere, and a cone. A one-to-many relationship can exist between features and shapes. Each record of the voxel table can include a foreign key to a shape identifier, wherein a one-to-many relationship exists between voxels and primitive shapes. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         FIG. 1A  is a schematic diagram showing a geospatial hybrid database in accordance with an embodiment of the disclosure. 
         FIG. 1B  is a schematic diagram distinguishing between voxel-level and feature-level semantics in accordance with an embodiment of the disclosure. 
         FIG. 1C  is a diagram showing a hybrid table and indexing between a voxel records and a feature records in accordance with an embodiment of the disclosure. 
         FIG. 2  describes an embodiment for populating and using a hybrid database and showing a relationship between envelopes, features, and voxels. 
         FIG. 3A  illustrates a set of tables for a hybrid database in accordance with an embodiment of the disclosure. 
         FIG. 3B  demonstrates aggregation efficiency of a hybrid database in accordance with an embodiment of the disclosure. 
         FIG. 4A  is a flow chart of a process to acquire hybrid database information from a data source in accordance with an embodiment of disclosure. 
         FIG. 4B  is a set of flow charts for utilizing data of a hybrid database in accordance with an embodiment of disclosure. 
         FIG. 5  is a schematic diagram of a system including a hybrid database in accordance with an embodiment of disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     The disclosure provides a volumetric storage space  120  of a voxel database, having a unified query system that queries attributes of voxel records and feature records. The volumetric storage space  120  can be a space composed of a set of volumetric units, called voxels  122 . Data elements can be directly referenced to voxels  122 , which permits these data elements to be spatially placed in the volumetric storage space  120 . The data elements need not have any specific identity outside their relationship to the voxels  122 , which permits raw data to be inserted into the volumetric storage space  120 . For example, satellite imagery, LIDAR points, and other information can all be inserted into the volumetric storage space  120  and referenced to voxels  122 . Viewed in one manner, each voxel  122  can be thought of as a three dimensional puzzle piece that fits together with other puzzle pieces to form the volumetric storage space  120 . Information included in the volumetric storage space  120  can be extracted post-storage. For example, outlines of objects can be detected within the volumetric storage space  120  to determine a presence or absence of a building, vehicle, crowd, or other object within the volumetric storage space  120 . 
     It should be noted that data elements can be continuously inserted into the volumetric storage space  120 . In this manner, data elements can be combined to continuously increase a “resolution” of the data image contained within the volumetric storage space  120 . In one embodiment, the volumetric storage space  120  can be a probabilistic one. In other words, data elements can be stored in the volumetric storage space  120  that have a probability of being contained therein but have a probability of not actually being contained therein. For example, if an incomplete “data image” of a building (which can be formed by 1 . . . N quantity of voxels) exists in the volumetric storage space  120 , an associated probability of the building being present in the volumetric storage space  120  can be at a value of forty percent where a sixty percent probability value exists that the building is not present in the volumetric storage space  120 . Thus, the volumetric storage space  120  is able to handle uncertainty of data elements in a manner that traditional storage spaces cannot. Probabilities can also apply to social characteristics and cultural conditions determined by processing data of the volumetric storage space  120 . 
     The volumetric storage space  120  can store data elements of any nature. For example, the data elements of the volumetric storage space  120  can include visual information in two or three dimensions. Data elements can also include material composition elements, elevation data, and the like. Any type of information that can be spatially related to a volumetric unit (e.g., voxel) can be stored in the volumetric storage space  120 . 
     Another way of expressing the volumetric storage space  120  is by using database terminology. Stated differently, each voxel  122  can have a unique identifier, which in a database system (e.g., database  130 ) can be a primary key of a database table having voxel records. Data elements of the volumetric storage space  120  can be attributes of the voxel records. Relative reference points of data elements within a corresponding voxel can be optionally recorded, should a spatial positioning of a data element be needed at a level of granularity less than a single voxel  122 . The only linkage of each data element within the database  130  can be defined by its relationship to a voxel  122 . That is, instead of referencing visual, material, or other characteristics of a building to that building, as would be the case with a standard database—visual, material, or other characteristics can be referenced directly to voxels  122 . 
     This ability to relate any number of characteristics (e.g., data elements) having a spatial component to the volumetric storage space  120  at a suitable spatial location (via voxel referencing) is significant and unique to a voxel database  130 . It is this ability that permits “raw” data to be directly inserted into the volumetric storage space  120 . The raw data (e.g., satellite data, for example) when acquired is typically formatted in a spatial manner well suited for proper insertion into the volumetric storage space  120 . Otherwise, input acquired from satellites (or similar sources) must be processed and categorized to specific objects (e.g., buildings, roads, etc). These objects are typically stored in databases as discrete entities having object specific attributes. Each time processing occurs, a data loss can result, as assumptions, which must be made during processing, may not be true. For example, during processing, material composition attributes are historically stored against to objects (e.g., buildings, roads, etc.) formed from these materials. There may be, however, uncertainty in which of a set of possible objects are actually present in a given spatial region. Thus, during processing, material composition attributes can be stored against the wrong objects. Conventional practices (that do not utilize a volumetric storage space  120 ) may attempt to correct for processing errors, as described above. Error correction techniques, however, do not change the fact that there is a fundamental disconnect with the paradigm used for storing data given the manner in which this data is acquired. Use of a volumetric storage space  120  is believed to resolve this disconnect, and believed to achieve numerous advantages as described herein. 
       FIG. 1A  shows a hybrid database  180  for storing geospatial data in accordance with an embodiment of the inventive arrangements disclosed herein. The hybrid database  180  stores some geospatially referenced information in voxel records  130  and stores other geospatially referenced information in feature records  134 . The voxel records  130  can define a volumetric storage space  120 , which is a volumetric space comprised of unique volumetric units, which are voxels  122 . Voxels  122  associated information can be stored in voxel table  192 , which has a unique identifier for each unique voxel  122 . In one embodiment, the real-world volumetric space  110  and units  112  therein can directly correspond to the storage volumetric space  120  and its units  122 . 
     The storage space  120  can also be mapped to a simulation space  140 , which is comprised of simulation units  141  (unique volumetric units of simulation space) that map to voxels  122 . The simulation space  140  can be a space that a simulator uses to create a representation of the real-world volumetric space  110 , which may be an interactive representation. The voxel records  130  can geospatially store information (indexed against unique voxels) in a raster format. Appreciably, the voxel records  130  can inherently store data in a probabilistic manner, as conflicting information from different sources  150  can be combined into a three dimensional volumetric space  120  that corresponds to a three dimensional real-world space  110 . 
     In contrast, the feature records  134  can store information in a vector format. Each feature of the feature records  134  can include a unique identifier for a feature, which is a type of real-world object. In one embodiment, feature specific object datum elements (e.g., feature characteristics) can be associated with certainty qualifiers, which indicate a degree of certainty to which the information is known. To distinguish the two, the probabilities of an item being certain in voxel records  130  can be dynamically computed based upon conflicting information of the records  130 . In feature records  134  probabilistic data (if any) is recorded as a value to an otherwise non-probabilistic discrete data element. As new information is incorporated into voxel records  130 , inherent probabilities of data certainty can change. Changing probabilities of feature records  134  certainty values requires an explicit changing of a value associated with an otherwise deterministic record. 
     In the feature records  134 , different types of real world objects can be equated to different types of features, where each type of feature can have a unique table (feature table  193 ) associated with it. Thus, feature records  134  can include a multitude of different feature tables  193 . For example, a tree can be a type of feature having a unique record in a tree table (one type of feature table). A building can be a type of feature having a unique record in a building table (another type of feature table). Different types of features can have different associated attributes and values, which permits different types of semantic data to be stored for the different feature types (i.e., trees and buildings are different from each other—searchable attributes recorded for each are expected to differ in a corresponding manner). A volume in storage space  120  occupied by a feature can be defined by a volumetric envelope (see envelope  234  of  FIG. 2 , for an example). The volumetric envelope can be composed of one or more shape primitives (see shapes  250  and  252  of  FIG. 2 , for an example). Shape primitives can be a set of basic volumetric shapes that are easily defined by a relatively small number of numeric parameters. 
       FIG. 1B  provides a diagram useful for distinguishing between voxel record content and feature record content. As shown, voxel-level semantics  190  can include visual attributes (color, contrast, intensity, brightness), spectral signature attributes (Multispectral Imaging (MSI), Hyperspectral Imaging (HSI)), material composition attributes, and the like. Voxel-level semantics  190  (e.g., data stored in voxel database  130 ) indicate that a data element “is part of a . . . ”; “is made of . . . ”; and/or “has an appearance of . . . ” something. 
     Feature-level semantics  192  can include a feature identifier, a feature type, and feature attributes, such a physical dimensions, geographic names, functional usage information, and the like. Feature-level semantics  192  (e.g., data stored in feature database  134 ) indicate that a data element “is a . . . ”; “has identifiable components consisting of . . . ”; “has feature specific attributes of . . . ”; and/or “has dimensions of . . . ” something. 
     Each feature of the feature records  134  can occupy a unique volume of volumetric space  120 , which corresponds to a set of unique voxels  122  of the voxel database  130 . 
     In one embodiment, a hybrid query engine  184  and hybrid outtake engine  186  can combine to permit uniform querying of the hybrid database  180 . That is (as shown by diagram  101  of  FIG. 1A ), a user  102  of a computer device  104  can issue a hybrid query  105 . The hybrid query  105  can be processed by the hybrid query engine  184 , where it is bifurcated into at least one voxel query and into at least one feature query. The voxel query can be a query submitted to voxel records  130 , which results produces voxel results  106 . The feature query can be a query submitted to feature records  134 , which produces feature results  107 . The hybrid outtake engine  186  can combine results  106 ,  107  to create hybrid response  108 . User  102  is able to issue their hybrid queries  105  to the hybrid database  180  without being aware (i.e., the hybrid querying process can be user  102  transparent) that database  180  stores some data in a vector based format and other data in a raster based format. 
       FIG. 1C  shows a diagram  195  for indexing between voxels and features in accordance with one embodiment of the disclosure. Diagram  195  is a simplified embodiment shown to clarify the underlying concepts and is not to be construed as limiting implementation details related to hybrid database  180  (or engines  184 ,  186 ). In diagram  195 , data image  196  represents an image of a data set from the voxel records  130 . Data image  197  is an image of a corresponding data set from the feature database  134 . 
     Hybrid attribute table  198  represents a table including combined information from records  130  and  134 , which is relevant for a given query (e.g., query  105 ). As shown in table  198 , each record is a feature record having a unique feature identifier. Feature type, length, width, and height attributes can be obtained from records  134  (feature data image  197 ) data. The material type attribute can be dynamically determined from information of voxel records  130  (voxel data image  196 ) data. Use of the hybrid attribute table  198  can permit a hybrid query  105  (which can be a structure query based query) to be directly performed to produce response  108 . That is, the actual query  105  need not be bifurcated into two separates queries, which produce results  106 ,  107 , which are later combined. 
     A different hybrid attribute table (not shown) could be constructed that is indexed by voxels, where feature specific information is incorporated. Thus, the combining of data between different databases  130 ,  134  and data sets ( 196 ,  197 ) is bidirectional. 
     Turning back to  FIG. 1A , the hybrid intake engine  182  can perform overhead and processing actions during information acquisition phases so that data sources  150  treat hybrid database  180  similar to a standard information repository. As shown by diagram  114 , incoming data  113  can contain semantic content that includes voxel-level semantics  190  and feature-level semantics  192  (shown in diagram  114  as hybrid data set image  116 ). Hybrid intake engine  182  can separate content of data  113  into volumetric attributes and feature characteristics, shown by bifurcated data set  117 . Volumetric attributes of data  113  can be those holding values for voxel-level semantics  190 , which are stored in voxel records  130 . Feature characteristics of data  113  can be those holding values for feature-level semantics  192 , which are stored in feature records  134 . 
     Diagram  152  shows an end-to-end process of converting data of real-world space  110  to storage space  120  to simulation space  140 . For a given geographic region, one or more data sources  150  can utilize a set of sensors to capture and record data  113  for a specific set of volume units  112 . Before converting data  113  into hybrid database  180  mapped records, the data  113  can be optionally normalized (by normalizer  160 ) to a definable standard. A data to volume mapping unit  162  can determine which unit  112  data  113  elements correspond to, should geospatial referencing be needed. Then, volume unit to voxel mapping component  164  can determine which voxel  122  corresponds to which volume unit  112 . A hybrid data encoder (of hybrid intake engine  182 ) can store the data  113  in hybrid database, which includes placing voxel-level semantics in voxel records  130  and feature-level semantics in feature records  134 . 
     Hybrid query engine  184  and outtake engine  186  can extract data from hybrid database  180 . Results  108  from engines  184 ,  186  can be geospatially related, which permits a mapping of the results to simulation space on a unit-by-unit basis. Thus, in one embodiment, data associated with a voxel unit  122  can be mapped to a corresponding simulation unit  141 . 
     Embodiment  210  of  FIG. 2  provides another description for populating and using hybrid database  180 . It also emphasizes that the database  180  can function as a central repository for a myriad of different types of data. Using embodiment  210  as a description reference, data (including data  113 ) captured from a real-world volumetric space  110  can be conveyed over a single pipeline to hybrid database  180 . The data  113  can come from many sources  150 , such as satellite imagery, digital elevation model (DEM) data, video, signals intelligence (SIGINT), human intelligence (HUMINT), and the like. Additionally, the hybrid database  180  can provide data for multiple different types of simulators (simulation space  140 ). In one embodiment, simulator specific filters  134  can be used to customize hybrid database  180  output for a use by a specific simulator. For example, behavioral simulations  142 , tactical engagement simulators (TES)  143 , constructive simulators  144 , immersion simulators  145 , vehicle simulators, and the like can all be operate on hybrid database  180  stored records. 
     The hybrid database  180  product can be a probabilistic one in which uncertainty is handled. In one embodiment, query engine  184  can include multiple different components for producing different queries (e.g., mission rehearsal query, training query, analysis query, etc.), which handle uncertainty in different manners for different types of consumers. It should be appreciated that embodiment  210  can be largely automated, which permits the process  212  from taking measurements, to producing simulation models to occur within minutes and not months, as is the case with conventional information gathering and modeling processes. 
     Diagram  230  provides an illustrated example for describing features. In diagram  230 , an envelope  234  of a voxel sub-space  232  can contain features  240  and  242 . Feature  240  can be uniquely identified as Feature0001, which is a feature identifier. The feature type of feature  240  can be a building. Feature  242  can be an air conditioning unit positioned on top of the building. As shown, each feature  240 ,  242  is formed from single shape primitives  250  and  252 , which are both boxes. Features can include any number (from 1 to N) of shape primitives. Each shape can include (be mapped to) a set of voxels. For example, three voxels  260  can form shape  250 . In one embodiment, the hybrid database  180  can include software implemented tools to automatically detect and define shapes, features, and envelopes in a given raster-based storage space or subspace. 
     While any number of shape primitives can be supported by hybrid database  180 , some common shape primitives include boxes, cylinders, spheres, and cones. 
     In one embodiment, shape primitives used by hybrid database  180  can conform to existing standards for enhanced compatibility. For example, shape primitives can conform to Open Graphics Library (OpenGL) standards for 3D computer graphics. In one embodiment, Coin3D, which is a C++ object oriented retained mode 3D graphics Application Program Interface (API) used to provide a higher layer of programming for OpenGL, objects can be mapped to shape primitives as follows: a box equates to a SoCube; a cylinder equates to a SoCylinder; a sphere equates to a SoSphere; and, a cone equates to a SoCone. In another embodiment, mappings to geospatial scheme of the National Geospatial-Intelligence Agency (NGA) can be as follows: a box equates to a RectangularPrism; a cylinder equates to a Vertical Cylindrical; a sphere equates to a spherical; and, a cone can have no equivalent. In still another embodiment, mappings to a computer aided design (CAD) scheme can be as follows: a box equates to an Axis Aligned Bounding Box (AABB); a cylinder equates to a Cylinder, Flat Ends; and, a sphere equates to a Cylinder, Round Ends, Zero Length/. 
       FIG. 3A  illustrates a set of tables  310 ,  320 ,  330 ,  340  for a hybrid database  180  in accordance with an embodiment of the disclosure. In one embodiment, the tables  310 ,  320 ,  330 ,  340  can be RDBMS tables in third normal form. This format is expressed in  FIG. 3A  for convenience and is not to be construed as a limitation on the scope of the disclosure. As shown, the tables  310 ,  320 ,  330 ,  340  can include a plurality of records. 
     Voxel table  310  (which stores voxel-level semantics  190 ) includes a VID  312 , which is a unique identifier for each voxel. SID  313  can be a unique identifier for a shape primitive which forms all or part of a shape envelope. Any quantity (1 . . . N) of attributes can be associated with each unique voxel of table  310 . 
     In one embodiment, certainty qualifiers can be placed on attributes of the tables  310 ,  320 ,  330 ,  340 , which can indicate a probability of that attribute accurately reflecting a real-world state. This can be true for not only vector-based data sets (those of feature tables  330 ,  340 ) but also for raster-based data sets (those of voxel table  310 ). Thus, voxel table  310  records can be encoded in a doubly probabilistic fashion. First, the information of table  310  can be internally inconsistent, because different data sources can report geospatial differences in different manners. Next, each recorded element can have an associated probability for when a single consistent data source notes an existence of a probability of a discrete reported data element. 
     To account for the second type of uncertainty, each attribute  314 ,  316  in the voxel table  310  can have at least two values, such as a lower value and an upper value. The multiple values can be used to record different levels of certainty for each attribute  314 ,  316 . For example, one source can report a first value of an attribute  314 ,  316  with a definable degree of certainty and a different value can be reported for the same attribute  314 ,  316  with a different degree of certainty. Although two values (lower and upper) are shown for each attribute  314 ,  316 , any number of values (1 . . . N) can be used in table  310 . 
     Table  330  and  340  can also include specific attribute  314 ,  316  values for uncertainty. In the feature tables  330 ,  340  this uncertainty can have a single nature, and can even reflect an uncertainty derived from the inherent uncertainty of raster stored data (due to inconsistent data being stored in a raster space) combined with a second probability value, such as from values of attributes  314  and  316 . 
     Each record in shape table  320  can include a unique shape identifier, SID  322 . A secondary key for a feature ID  324  can also be included. Table  320  can also include a type  326  attribute. A set (0 . . . N) of additional shape specific attributes  328  can also exist. 
     Each unique feature can be associated with a feature identifier, FID  332 . In one implementation, different types of tables  330 ,  340  can exist, one for each unique category or type of object, which corresponds to a feature. For example, one table  330  can exist for buildings and another table  340  can exist for tree groves. Each table  330 ,  340  can have an associated set of attributes  334 ,  344 , which are unique to a specific type of object. It should be appreciated that arrangements of tables  310 ,  320 ,  330 ,  340  are presented to illustrate a concept expressed herein and are not to be construed as a limitation of the disclosure. 
     Diagram  350  (shown in  FIG. 3B ) illustrates how voxel database  130  is able to efficiently aggregate information. This aggregation efficiency actually accelerates as information density increases. For example, as a number of images encoded within voxel database  130  increases, storage requirements can actually decrease (or at least become more efficient than the straight line increase experienced using a pure traditional vector-based GIS). Aggregation efficiency results from the “holographic-like” nature of voxel storage space, where an increase in information density increases clarity of the storage space  120 . Uncertainty is reduced, which can reduce storage requirements (e.g., decreasing overhead needed for maintaining “noise” or abnormal data points in storage space  120 ). 
     Aggregation efficiency of the voxel database  130  is represented in diagram  350  by a set of images  360 - 366  of a stored volumetric space. The images  360 - 366  are static geospatial images of real-world terrain taken from satellite images, yet the demonstrated principle is consistent regardless of the specific input being encoded in a volumetric storage space. 
     Image  360  shows a visual depiction of a raster-based storage space formed from ten images. Image  362  shows the same storage space after 20 images have been processed. Image  364  shows the storage space after 30 images. Image  366  shows same storage space, that has been refined using LIDAR points in conjunction with the thirty images. As shown, it becomes evident that an increase in information density decreases uncertainty of an encoded raster-based storage space and increases “fidelity” of the stored information. That is, as information density increases surface probabilities become better defined. More voxels (and associated data) in “empty space” can be discarded. 
     It can be mathematically shown that as information density approaches infinity, storage space requirements for the voxel database  130  approaches (effectively equals) a theoretical minimal storage space required by the imagery (and/or data elements being stored). At relatively low information densities (compared to that currently being handled by intelligence agencies) a cross-over point  354  occurs, where it is more efficient to store equivalent data within voxel records  130  than it is to store equivalent data in a non-voxel database (e.g., a conventional raster or image based storage medium). Post cross-over point  354  voxel records  130  storage space advantages continue to increase, as shown by chart  352 . It should be noted that although many examples presented herein are in context of intelligence activities, voxel records  130  aggregation efficiencies and techniques are domain independent can be used for any geospatial data set. 
       FIG. 4A  shows a process  410  to acquire voxel database  430  information from a data source  150  in accordance with an embodiment of disclosure. In process  410  data can be continuously received from a variety of sources, which include completely automated data capture sources (step  420 ), human data sources (step  422 ), and generating new intelligence data (or other information) by analyzing and combining existing source data (step  424 ). This data can be continuously being handled by the process, as represented by process  410  proceeding from step  440  to steps  420 ,  422 , and/or  424 . In process  410 , data acquisitions and processes can occur in real-time or after an appreciable delay (e.g., handled in batch) depending upon implementation choices. Further, process  410  actions can occur asynchronously/synchronously as well as cyclically/randomly/based on conditional events depending on contemplated implementation choices. 
     Regardless of how raw data is gathered (step  420 ,  422 , or  424 ), the data can be optionally processed as needed, as shown by step  426 . In step  428 , the raw data can be correlated to volumetric geospatial units and/or to populations present in the units. For example, data can be mapped to absolute or relative points in geographic space. In step  430 , a degree or level of confidence for the mapped data elements can be determined. In optional step  432 , data elements can be classified in accordance to a source type and/or a specific data source can be tagged or otherwise related to the data elements. 
     The data elements can be recorded in a hybrid database  180  comprising a raster-based volumetric storage space voxel space (e.g., voxel database  130 ), as shown by step  434 . When data elements are recorded in the hybrid database, a determination can be made as to whether each data element is to be referenced against a set of one or more voxels, against a defined feature, or both, as indicated by step  436 . This decision can be based on whether the data being recorded includes voxel-level semantics  190  or feature-level semantic  192 . 
     In optional step  438 , data can be semantically optimized to minimize data redundancy. For example, approximately equivalent data from multiple sources can be combined into a common data element. This semantic combination can affect confidence values associated with a data element. For example, when multiple sources report a single data element consistently, a confidence value in that data element will increase. In optional step  440 , a volumetric storage space can be compacted to minimize storage requirements. For example, different voxel (e.g., raster based) compaction algorithms can be utilized to minimize storage needs of voxel records  130 , which include loss-less compaction algorithms and lossy compaction algorithms. 
     The hybrid database  180  populated though a process, such as process  410 , can thereafter be treated as a common repository or centralized source for geospatially related information. This centralized source can be utilized by different consumers in different ways. In one scenario (process  450  shown in  FIG. 4B ), the hybrid database can be used to generate a non-voxel based product. In another scenario (process  470  shown in  FIG. 4B ), the hybrid database can provide voxel-subspace data sets to requestors, which these requestors can consume directly utilizing an internal voxel engine. 
     Process  450  can begin in step  452 , where a request is received by a hybrid database server. The request can be for creating a tailored non-voxel based product from a common voxel based product. An appropriate converter for the request can be determined in step  454 . 
     In step  456 , a relative portion or volume of storage space needs to be determined. That is, the request will rarely be for an entire volume region stored by the hybrid database, but will likely be for a volumetric subspace specifically needed by the non-voxel based product. Additionally, data within the requested volumetric subspace can be filtered by applied data filters, so that only the information needed for a specific product of the request is considered. In step  458 , probabilistic parameters can be utilized to negate uncertainty inherent in the hybrid database when generating the non-voxel based product. Different thresholds and/or parameters can be utilized to determine what level of uncertainty is to be retained within the non-voxel based product, which is generated in step  460 . The generated product can be delivered to the requestor in step  462 . 
     Some generated products can require periodic updates form the hybrid database in order to retain information currency. In one embodiment, optimizations can be implemented so that only relatively new information needs to be considered for some update operations. When iterative updates are a concern, information can be logged and/or time attributes of the hybrid database can be updated as appropriate, which is shown by step  464 . The process  450  can repeat as needed, which is expressed by proceeding from step  464  to step  452 . 
     Process  470  can begin in step  472 , where a request for a volumetric sub-space is received. The request can have a set of associated filters. Unlike process  450 , it is contemplated that a requestor of process  470  can directly consume voxel encoded information. In step  474 , the filter can be applied to the volumetric sub-space to conditionally exclude data of the hybrid database. This is important as the hybrid database can be a centralized repository that stores a myriad of data attributes, where only a subset of the data attributes are of concern for a specific requestor. In optional step  475 , probabilistic parameters can be applied to negate uncertainty when generating the volumetric sub-space. This optional step  475  can be utilized when satisfying a request (step  472 ) for a non-probabilistic subspace. 
     In step  476 , a file (or set of files) containing the requested information can be created. In step  478 , the created file(s) can be delivered to a requesting client, such as by delivering the file(s) over a network. A voxel engine of the client can consume or utilize the sub-space file, as shown by step  480 . In one embodiment, the hybrid database can be directly accessible and used by the clients, in which case a creation and utilization of a locally create file can be unnecessary. 
     In one embodiment, the sub-space files can be encoded in a local media storage area (e.g., hard drive) for use by a client as needed. This prevents a need for continuous and/or stable network connectively between the client and the hybrid database. In one embodiment, suitable sub-space laden files can be encoded in a portable medium (e.g., optical, magnetic, or other) and disseminated/located to clients periodically. 
     In another embodiment, data sets can be continuously requested by a client. That is, executing client code can trigger a need for another volume of storage space, as shown by step  482 . When no local cache exists for this needed information, a new hybrid database request (submitted over a network) can be created, as shown by step  484 , which results in the request being handled in step  472 . 
       FIG. 5  is a schematic diagram of a system  500  including a hybrid database  180  in accordance with an embodiment of the inventive arrangements disclosed herein. In system  500 , a set of data sources  150 , a set of simulation devices  506 , an intake server  510 , an outtake server  520 , a hybrid database  180 , and other such components can be communicatively linked via a network  560 . In lieu of connectivity via network  560 , components of system  500  can exchange information via portable media data exchanges, paper document correspondences, human-to-human communications, and the like. The shown components (as items  150 ,  510 ,  520 ,  506 ,  180 ) represent one embodiment of the disclosure and are not to be construed as being a limitation of the disclosure&#39;s scope. 
     Various components of system  500 , such as items  150 ,  510 ,  520 ,  506 ,  180 , can include one or more computing devices  570 , which can include hardware  580  and computer program products  590 . The computing devices  570  can be general purpose computing devices, such as personal computers, servers, or in-vehicle computers. The devices  570  can also be special purposed devices specifically manufactured/constructed for a tailored purpose. A special purposed device can have unique hardware, electronic boards, firmware, etc, which is not able to be easily modified by software and used for a different purpose. In various embodiments, devices  570  can be implanted as stand-alone devices, as virtual devices, as distributed devices, as cooperative devices, and the like. 
     Hardware  580  can include a processor  582 , nonvolatile memory  583 , volatile memory  584 , network transceiver  585 , and other components linked via a bus  586 . The computer program products  590  can include programmatic instructions that are digitally encoded in a memory (e.g., memory  583 ,  584 ) and able to be executed by the processor  582 . Computer program products  590  include boot firmware  592 , (e.g., basic input/output system (BIOS)), an optional operating system  593  (i.e., special purposed devices can be optimized so an operating system  593  is merged with applications  594  and/or modules  595 ), applications  594 , and other executable modules  595 . The operating system  593  can include mobile device operating systems, desktop operating systems, server operating system, virtual operating systems, and/or distributed operating systems. 
     Unlike many computing systems, system  500  can be a security sensitive one where data classifications are highly important. That is, information acquired from data sources  150 , stored in hybrid database  180 , and used to drive simulation devices  506  can include unclassified, secret, top secret (including compartmentalization) information. Classification components  504 ,  514 ,  524  can exist, which implement comprehensive and conservative rules to automatically classify information into appropriate classifications. Additionally, sanitizers (e.g., sanitizer  526 ) can be used in system  500  to downgrade semantic content (e.g., from secret to unclassified, for example) of conveyed data elements to ensure that classification based restrictions are not violated. Moreover, different network  560  channels and information handling standards can be imposed based on classification level of the information being conveyed. A further complication is that aggregating and/or analyzing data from different sources  150  can change a classification level of the base data. Automated mechanisms (i.e., classifier  514 , aggregator  528 , and/or hybrid database  180 , when aggregating data from multiple sources  150 , can reevaluate and appropriately adjust resultant security classification levels) to conservatively handle data classifications are needed in system  500 , especially in embodiments where data acquisition to model production (e.g., duration  212  of embodiment  210 , for instance) is expedited. 
     The security sensitivity requirements can result in physically separate channels (e.g., within network  560 , for example) for information conveyance. Further, storage regions for the different data classifications (e.g., within hybrid database  180 , for example) can remain isolated from each other. Known standards for handling classified information exist as do a myriad of automated techniques, which can be utilized for system  500 . Various components (classifier  504 ,  514 ,  524 , security manager  542 , sanitizer  526 ) are shown in system  500  to express that system  500  can implement security classification technologies. Comprehensive coverage of these known technologies is not the focus of this disclosure. For simplicity of expression, classification techniques have not been overly elaborated upon herein. It should be understood that integration of classification specific techniques for information handling are contemplated for the disclosure. 
     It should also be acknowledged that the specific arrangements of system  500  are expected to vary from implementation-to-implementation. For example, discrete network  560  attached servers are shown for intake (intake server  510 ) and outtake (outtake server  520 ) of information to and from the hybrid database  180 . As shown, intake server  510  can perform intake processing operations (process  410 , for example). Outtake server  520  can perform out taking processing operations (process  450  and/or  470 , for example). In one embodiment, operations attributed to server  510  or  520  can be integrated into the hybrid database  180  or other system  500  components (e.g., one or more intake server  510  operations can be performed by data source  150 ; one or more outtake server  520  operations can be performed by simulation device  506 ). For example, in one embodiment, pre-processing unit  502  can optionally perform operations described for normalizer  160  and/or data to volume unit mapping component  162 . 
     Additional components not explicitly expressed in association with system  500 , which are consistent with performing operations described in the disclosure, are to be considered present in system  500 . Further, logical mappings from system  500  components to operations described herein are assumed to be present. For example, in various contemplated embodiments, compactor  544  can perform operations described in step  440  of  FIG. 4A ; semantic optimizer  546  can perform operations described in step  438  of  FIG. 4A ; and, confidence adjustor  516  can perform operations previously described in step  430  and  438 . 
     Turning to hybrid database  180 , a number of characteristics should be noted. First, as new information for hybrid database  180  is acquired (from data sources  150 ); a probability distribution of surface location and surface appearance can be dynamically and programmatically constructed (using Bayesian statistical learning algorithms, for example). In this sense, voxels of the voxel records  130  do not store a fixed appearance (of volume units  112  from a real-world volumetric space  110 ) but instead store a dynamic probability of multiple appearances, which can be learned and/or refined over time. 
     This characteristic of hybrid database  180  not only permits efficient handling of uncertainty, but turns traditional data overload challenges into an advantage. That is, over time, information acquisition via satellites, SIGINT, and other automated sources has geometrically increased. Concurrently, a quantity of human analysts responsible for rapidly responding to acquired information has decreased and/or remained constant. In the past, different information channels or products from different sources  150  were handled in a stove-piped manner. Different human analysts would receive and/or analyze satellite data, SIGINT data, HUMINT, and the like. One result of this situation is that collected data is often not analyzed in a timely manner. Additionally, collected data is typically analyzed in isolation (e.g., single images from satellites are analyzed by people lacking pertinent geospatial related data from other sources  150 ). Fusion tools are currently deficient and/or lacking, which is a situation expected to worsen in absence of a paradigm shift in how information is managed and analyzed. The hybrid database  180  is a central component for this needed paradigm shift. 
     The disclosure may be embodied as a method, system, or computer program product. Accordingly, the disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module,” or “system.” Furthermore, the disclosure may take the form of a computer program product on a computer-usable storage medium having computer-usable program code embodied in the medium. In a preferred embodiment, the disclosure is implemented in software which includes, but is not limited to firmware, resident software, microcode, etc. 
     Furthermore, the invention can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer-readable medium can be any apparatus that can contain, store, communicate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. Any suitable computer-usable or computer-readable medium may be utilized. The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM) or Flash memory, a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk-read only memory (CD-ROM), compact disk-read/write (CD-R/W) and DVD. 
     Computer program code for carrying out operations of the disclosure may be written in an object-oriented programming language such as JAVA, Smalltalk, C++, or the like. However, the computer program code for carrying out operations of the disclosure may also be written in conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through a local area network (LAN), a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). 
     A data processing system suitable for storing and/or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code in order to reduce the number of times code must be retrieved from bulk storage during execution. 
     Input/output or I/O devices (including, but not limited to, keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening I/O controllers. 
     Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters. 
     The disclosure is described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The diagrams in  FIGS. 1-5  illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.