Abstract: The bionic computing system includes a perception subsystem, an attention subsystem, and a temporal-spatial awareness subsystem. The perception subsystem has several perceptual devices for detecting objects from sequences of sensory data and generating an object record for each object. The attention subsystem adjusts the object records by re-identifying the tracking identities across sensory devices, generates several object associations, generate several location associations, and generates several motion implications. The temporal-spatial awareness subsystem organizes and retains the object records in a working memory space.

Abstract: A federated data exploration system includes a first and a second interoperable data exploration apparatuses. The first interoperable data exploration apparatus executes a data process engine and has a first data object. The data process engine enables a display screen to display a visualization user interface, which includes a first and second display areas respectively for the first and second data object. The second data object belongs to the second interoperable data exploration apparatus. The data process engine explores the second data object by issuing a sequence of queries in response to a sequence of operations on the second display area to narrow down a target of interest. The data process engine receives a first set of identifiers corresponding to the target of interest from the second data object and produces a target dataset by querying the first data object according to the first set of identifiers.

Abstract: A federated data exploration system includes a first and a second interoperable data exploration apparatuses. The first interoperable data exploration apparatus executes a data process engine and has a first data object. The data process engine enables a display screen to display a visualization user interface, which includes a first and second display areas respectively for the first and second data object. The second data object belongs to the second interoperable data exploration apparatus. The data process engine explores the second data object by issuing a sequence of queries in response to a sequence of operations on the second display area to narrow down a target of interest. The data process engine receives a first set of identifiers corresponding to the target of interest from the second data object and produces a target dataset by querying the first data object according to the first set of identifiers.

Abstract: Global tracking systems and cloud systems thereof are provided. Several image capturing devices are deployed in a space. Each image capturing device sees a pre-designated view area in the space, and the space has blind area(s) uncovered. The processor(s) receives a sequence of images from each image capturing device, detect several object instances from the images, and generate an object record for each object instance. Each object record has a tracking identity, a timestamp, and a geographical coordinate where the corresponding object located. The object records with the same tracking identity correspond to the same object and the same image capturing device. The processor(s) determines that a specific object has no object record within a pre-determined time interval, finds a specific object record corresponding to a last appearance of the specific object, and projects that the specific object is entering into a specific blind area in the pre-determined time interval.

Abstract: The bionic computing system includes a perception subsystem, an attention subsystem, and a temporal-spatial awareness subsystem. The perception subsystem has several perceptual devices for detecting objects from sequences of sensory data and generating an object record for each object. The attention subsystem adjusts the object records by re-identifying the tracking identities across sensory devices, generates several object associations, generate several location associations, and generates several motion implications. The temporal-spatial awareness subsystem organizes and retains the object records in a working memory space.

Abstract: A data tracking apparatus, method, and non-transitory tangible machine-readable medium thereof are provided. The data tracking apparatus includes a storage device, a display screen, and a processor. The storage device stores a fact dataset, wherein the fact dataset is continuously updated by a stream of data. The display screen displays a user interface, wherein the user interface comprises a plurality of options related to the fact dataset. The processor establishes a tracking rule in response to one of the options being selected and updates the user interface so that the user interface further includes a tracking item corresponding to the tracking rule. The processor derives a tracking result by querying the updated fact dataset according to the tracking rule in response to the tracking item being selected.

Abstract: A data tracking apparatus, method, and non-transitory computer readable storage medium thereof are provided. The data tracking apparatus receives a stream of data continuously and stores a fact dataset, wherein the fact dataset is updated by the stream of data continuously. The fact dataset includes a plurality of fact records, wherein each of the fact records includes an identity value. The data tracking apparatus locates a tracking target by querying the fact dataset corresponding to a first time instant according to a first query command, wherein the tracking target includes a subset of the identity values. The data tracking apparatus derives a tracked dataset by querying the fact dataset corresponding to a second time instant according to the tracking target and a second query command.

Abstract: Data processing apparatus, method, and non-transitory tangible machine-readable medium thereof for recursively structured data are provided. The data processing apparatus accesses a piece of recursively structured data having a Location Independent Structure and arranges the piece of recursively structured data in a one-dimensional memory space according to LIS:=e0?e1? . . . ?ep?1?LIS0?LIS1? . . . ?LISp?1. The notation ei represents an element of the piece of recursively structured data. The notation LISi represents a sub-LIS of the recursively structured data and corresponds to the element ei. The operator ‘?’ indicates “contiguous to” in the one-dimensional memory space. When the elements in all depths are of the element size and the LIS sizes of the sub-LISs within the same depth are the same, the piece of recursively structured data is a complete LIS.

Abstract: Data management apparatus, method, and non-transitory tangible machine-readable medium thereof are provided. A memory space of an in-memory database is allocated for a sliding table, wherein the sliding table includes a plurality of records stored in a sequential order according to a time stamp of each record. A first head pointer points to the beginning address of the least recent record among the records, while a tail pointer points to the following address of the most recent record among the records. When at least one new record comprising a time stamp is inserted into the sliding table, the tail pointer will be updated. When at least one expired record of the sliding table is identified according to a preset time bound and the time stamp of each expired record, the head point will be updated.

Abstract: Data processing apparatus, method, and non-transitory tangible machine-readable medium thereof for recursively structured data are provided. The data processing apparatus accesses a piece of recursively structured data having a Location Independent Structure and arranges the piece of recursively structured data in a one-dimensional memory space according to LIS:=e0?e1? . . . ?ep?1?LIS0?LIS1? . . . ?LISp?1. The notation ei represents an element of the piece of recursively structured data. The notation LISi represents a sub-LIS of the recursively structured data and corresponds to the element ei. The operator ‘?’ indicates “contiguous to” in the one-dimensional memory space. When the elements in all depths are of the element size and the LIS sizes of the sub-LISs within the same depth are the same, the piece of recursively structured data is a complete LIS.

Abstract: Database querying apparatuses, methods for querying a database, and non-transitory tangible machine-readable media thereof are provided. The database querying apparatus includes receives a query including an adjective, an attribute, and a dataset name. The database querying apparatuses is stored with a fact dataset, which is defined with the attribute and at least one data field. The fact dataset includes at least one fact record. Each fact record includes an attribute value corresponding to the attribute and a list of data corresponding to the at least one data field. The database querying apparatuses accesses the fact dataset according to the dataset name, divides the fact dataset into several groups according to the attribute values, apply the operation corresponding to the adjective to each group to derive several sub-results, and generate the final result for the query from the sub-results. The operation corresponds to the adjective.

Abstract: Data processing apparatuses and data processing methods are provided. The data processing apparatus includes a storage unit, an interface, and a processor. The storage unit stores a first key-value set including a plurality of first key-value pairs. Each of the first key-value pairs has a first key and a first value. The interface receives an instruction regarding a set operation and a function. The processor generates a plurality of second keys by applying the set operation to the first keys. Each of the second keys includes at least one of the first keys. The processor generates a second value for each of the second keys by applying the function to at least one datum corresponding to the at least one first key comprised in the second key. Each second key and the corresponding second value form a second key-value pair and the second key-value pairs form a second key-value set.

Abstract: Data processing apparatuses, methods, and non-transitory tangible machine-readable medium thereof are provided. The data processing method accesses a dimension table. The dimension table is defined with a plurality of attributes and includes at least one member. Each of the at least one member includes a plurality of attribute values corresponding to the attributes. The data processing method generates a smart index for each of the distinct attribute values. Each of the smart indexes includes a first value equivalent to one of the attribute values, a second value equivalent to the attribute corresponding to the first value, and a third value indicating a rank of the first value comparing to the rest attribute values corresponding to the second value, the first values are distinct. The data processing method integrates the smart indexes into a smart index record, wherein each of the smart indexes has an index within the smart index record.

Abstract: Data processing apparatuses and data processing methods are provided. The data processing apparatus includes a storage unit, an interface, and a processor. The storage unit stores a first key-value set including a plurality of first key-value pairs. Each of the first key-value pairs has a first key and a first value. The interface receives an instruction regarding a set operation and a function. The processor generates a plurality of second keys by applying the set operation to the first keys. Each of the second keys includes at least one of the first keys. The processor generates a second value for each of the second keys by applying the function to at least one datum corresponding to the at least one first key comprised in the second key. Each second key and the corresponding second value form a second key-value pair and the second key-value pairs form a second key-value set.

Abstract: A large-scale data processing apparatus, method, and non-transitory tangible machine-readable medium are provided. The large-scale data processing apparatus includes a storage unit, an interface, and a processor. The storage unit is stored with a measure table comprising at least one measure. Each of the at least one measure includes a value corresponding to a key attribute and a piece of data corresponding to a data field. The interface is configured to receive a level order of N attributes, wherein N is a positive integer and the N attributes comprise the key attribute. The processor is configured to create a measure tree object for the measure table by following the level order so that the measure tree object has N levels corresponding to the N attributes in a one-to-one fashion.

Abstract: A large-scale data processing apparatus, method, and non-transitory tangible machine-readable medium are provided. The large-scale data processing apparatus includes a storage unit, an interface, and a processor. The storage unit is stored with a measure table comprising at least one measure. Each of the at least one measure includes a value corresponding to a key attribute and a piece of data corresponding to a data field. The interface is configured to receive a level order of N attributes, wherein N is a positive integer and the N attributes comprise the key attribute. The processor is configured to create a measure tree object for the measure table by following the level order so that the measure tree object has N levels corresponding to the N attributes in a one-to-one fashion.

Abstract: A large-scale data processing system, a large-scale data processing method, and a non-transitory tangible machine-readable medium are provided. The large-scale data processing system comprises an interface and a processor. The interface accesses a multi-dimensional data model, wherein the multi-dimensional data model comprises a plurality of dimensions, the dimensions form a multi-dimensional space of measures, each dimension is a single space comprising a plurality of members with a common set of attributes, and each measure is a data element organized and accessible through the multi-dimensional space of the cross-product of all dimensions. The processor builds at least one Tree Object (TO), wherein the TO is derived by converting the multi-dimensional data model into an N-level tree data structure according to a level order of N attributes, each tree node in the TO meets all conditions of attributes for all ancestor nodes, and N is a positive integer.

Abstract: A large-scale data processing system, a large-scale data processing method, and a non-transitory tangible machine-readable medium are provided. The large-scale data processing system comprises an interface and a processor. The interface accesses a multi-dimensional data model, wherein the multi-dimensional data model comprises a plurality of dimensions, the dimensions form a multi-dimensional space of measures, each dimension is a single space comprising a plurality of members with a common set of attributes, and each measure is a data element organized and accessible through the multi-dimensional space of the cross-product of all dimensions. The processor builds at least one Tree Object (TO), wherein the TO is derived by converting the multi-dimensional data model into an N-level tree data structure according to a level order of N attributes, each tree node in the TO meets all conditions of attributes for all ancestor nodes, and N is a positive integer.

Abstract: An apparatus and method for realizing big data into a Big Object and a non-transitory tangible machine-readable medium are provided. The apparatus comprises an interface and a processor. The interface is configured to access big data stored in a storage device. The processor is configured to create the Big Object from the big data using memory mapped files. The processor further lays out a content of the Big Object, wherein the content comprises a meta information section, a tree section, and a data section. The processor further lays out a content of the tree section by using LIS and describes a structure of the Big Object in the meta information section.

Abstract: A collaborative method and its implementation system for a trust-based business community, enables a business person, who behaves as a function of a corporation, to invite his/her business partners to form a trust-based business community. Through this method and system, the business person can create, conduct and commit/abort a collaborative activity with their business partners to achieve a business objective. This method and system are also defined as a participation mechanism, which effectively addresses the mixed situations of competition and collaboration. Based on legal-binding invitation and principle of good faith, the system can support the establishment of such a TBC, thus providing a good collaboration platform for the companies to do business effectively and efficiently in today's business world.