Abstract: The present disclosure relates to an apparatus and a method for processing road situation data. A road situation data processing method according to an exemplary embodiment of the present disclosure is a road situation data processing method which is performed by a processor of an apparatus for processing road situation data including: collecting sensing data on objects on a road from a plurality of sensors provided on the road; modeling a relationship between the objects on a graph based on sensing data on the objects; constructing a grid-based spatial index with respect to the graph modeling result; removing redundant sensing data among sensing data on the objects included in the grid-based spatial index; extracting an object corresponding to a response to a query by performing a predetermined query on the objects from which the redundant sensing data is removed; and outputting context awareness data to the object corresponding to the response to the query.

Abstract: Disclosed are a method and a system for providing a traffic information-based social network service, which provides traffic information between vehicles or between a vehicle and a user terminal through a social network service, wherein traffic information is shared between vehicles or between a vehicle and the mobile device of a person through a social network service.

Abstract: A recovery method for a high-dimensional index structure is disclosed, in which a reinsert operation is employed based on ARIES (algorithm for recovery and isolation exploiting semantics) and a page-oriented redo and a logical undo. Further, a recording medium on which a program for carrying out the above method is recorded is disclosed, the program being readable by a computer. The recovery method for a high-dimensional index structure employing a reinsert operation according to the present invention includes the following steps. At a first step, an entry is inserted into a node, a minimum bounding region is adjusted, an overflow is processed, and a log record is stored. At a second step, the log record thus stored is recovered.

Abstract: A bulk loading method, for use in a high-dimensional index structure using some parts of dimensions based on an unbalanced binarization scheme, accelerates an index construction and improves a search performance. For the purpose, the bulk loading method calculates a topology of the index by recognizing information for the index to be constructed using a given data set, splits the given data set into sub-sets of data by repeatedly performing an establishment of a split strategy and a binarization based on the calculated topology of the index, if a leaf node is derived from the sub-sets of data divided through a top-down recursive split process, reflects a minimum bounding region of the leaf node on a higher node, and, if a non-leaf node is generated, repeatedly performing the above processes for another sub-set of data to thereby produce a final root node.

Abstract: A recovery method for a high-dimensional index structure is disclosed, in which a reinsert operation is employed based on ARIES (algorithm for recovery and isolation exploiting semantics) and a page-oriented redo and a logical undo. Further, a recording medium on which a program for carrying out the above method is recorded is disclosed, the program being readable by a computer. The recovery method for a high-dimensional index structure employing a reinsert operation according to the present invention includes the following steps. At a first step, an entry is inserted into a node, a minimum bounding region is adjusted, an overflow is processed, and a log record is stored. At a second step, the log record thus stored is recovered.

Abstract: A concurrency control method for searching the high-dimensional index tree of a database is disclosed. The concurrency control includes: a) adding a root node to the queue and acquiring the shared lock for reinsertion node; b) determining whether the queue is empty or not, fetching a node from the queue and assigning the fetched node as a current node if queue is not empty, releasing the shared lock and terminating the search process if queue is empty; c) acquiring the shared latch in the current node, selecting the lower nodes which are within the query range and adding the selected nodes to the queue if current node is not leaf or to the result set if current node is leaf; and d) returning to the step b).

Abstract: A concurrency control method for a high dimensional index structure that provides efficient concurrency control method for a high dimensional index structure, which performs reinsertion of certain objects to cope with node overflow. The concurrency controlled searching method includes the following steps. First, an entry is obtained from a queue storing the root node and an object relating to the entry is selected. Second, whether a logic sequence number (LSN) of a lower level node is larger than an expected LSN stored in the upper node is determined. Third, the process moves to a neighbor node of the lower level node if the LSN is bigger than an expected LSN stored in the upper node in the second step, selects a relating object, and performs from the second step repeatedly.

Abstract: A bulk loading method, for use in a high-dimensional index structure using some parts of dimensions based on an unbalanced binarization scheme, accelerates an index construction and improves a search performance. For the purpose, the bulk loading method calculates a topology of the index by recognizing information for the index to be constructed using a given data set, splits the given data set into sub-sets of data by repeatedly performing an establishment of a split strategy and a binarization based on the calculated topology of the index, if a leaf node is derived from the sub-sets of data divided through a top-down recursive split process, reflects a minimum bounding region of the leaf node on a higher node, and, if a non-leaf node is generated, repeatedly performing the above processes for another sub-set of data to thereby produce a final root node.