14 results about "Self-balancing binary search tree" patented technology

Systems and methods for using a scalable collection within a balanced tree data structure are provided herein. In particular, techniques for generating scalable collections and using scalable collections for fetching data corresponding to an application using balanced chunked data tree structures are provided herein. In an example, the method may include determining a document defined by a balanced tree data structure having a root chunk, reference chunks, and chunks each having nodes each corresponding to a document attribute. The nodes may include a scalable collection containing a list node and child nodes associated with the list node, and a placeholder node that provides a reference to another chunk based on a position of the placeholder node within a respective chunk. The method may also include navigating to content within the document based on the balanced tree data structure.

The invention provides an online education resource course label generation method and system, and the method comprises the steps: carrying out the label extraction of the text data of a to-be-generated online education resource, and obtaining a label set; labels in the label set are converted into high-dimensional vectors through a preset word vector model, and first similarity information between the high-dimensional vectors is obtained; constructing a binary balance tree of the high-dimensional vector according to the first similarity information through a preset binary K-means clustering algorithm; extracting subject terms from the text data of the curriculum brief introduction of the to-be-generated online education resources; converting the subject term into a subject term vector, and obtaining second similarity information of the subject term vector and the high-dimensional vector; and determining the subject term vector and the high-dimensional vector corresponding to the third similarity information as the course tag of the to-be-generated online education resource.

Techniques are described for fast large file truncation. An example method includes receiving, by a data platform, a request to truncate a file, wherein data of the file is stored in a tree data structure including a plurality of nodes with leaf nodes corresponding to data of the file; determining a first node of the plurality of nodes including a plurality of child nodes; determining, based on a maximum key, a subset of the child nodes corresponding to a portion of the data that is to be retained; based on determining not to traverse the first node, updating a second node that is a parent of the first node to include a pointer to each child node of the subset of child nodes; and moving, from a subset of leaf nodes referenced by the subset of child nodes, a leaf node to rebalance the tree data structure.

The invention discloses a data reading method and a related device, and relates to the field of data storage. And the server receives a first processing request which is from the client and is used for indicating to acquire the fragmentation boundary information of the target data, and returns the fragmentation boundary information of the target data to the client by scanning the target self-balancing tree corresponding to the target data in response to the first processing request. And the server side receives a second processing request which is from the client side and is used for indicating to obtain the P fragments of the target data, and further returns the P fragments of the target data to the client side in a fragment form in response to the second processing request. The fragment boundary information of the target data comprises a value of an identifier of the target data stored in a root node of the target self-balancing tree or a child node of the root node. By taking the value of the identifier of the target data stored in the root node or the child node of the root node as the fragment boundary information, the speed of determining the fragment boundary information is accelerated, and the efficiency of performing data reading on the P fragments of the target data is improved.

Systems and methods for using a scalable collection within a balanced tree data structure are provided herein. In particular, techniques for generating scalable collections and using scalable collections for fetching data corresponding to an application using balanced chunked data tree structures are provided herein. In an example, the method may include determining a document defined by a balanced tree data structure having a root chunk, reference chunks, and chunks each having nodes each corresponding to a document attribute. The nodes may include a scalable collection containing a list node and child nodes associated with the list node, and a placeholder node that provides a reference to another chunk based on a position of the placeholder node within a respective chunk. The method may also include navigating to content within the document based on the balanced tree data structure.

The invention discloses a method for constructing an interval search balance tree based on batch insertion. The method comprises the following steps of: 1) setting an element quantity threshold value of a node; step 2), calculating the number of nodes of the next level according to the total number of the nodes of the current level and the element number threshold value of the nodes; 3) sorting all the nodes of the current hierarchy along an X axis, and dividing the nodes with close positions into multiple columns according to an X-axis sorting result; (4) sorting the nodes in each column along the Y axis, dividing the nodes with close positions in each column into a plurality of sections according to a Y-axis sorting result, and inserting the nodes in each section into a father node; and step 5), processing all the father nodes at the next level in a recursion mode until the father nodes reach root nodes, and obtaining a balance tree. According to the method, the interval search balance tree can be quickly constructed, and the efficiency of area search initialization and batch operation of processing elements is effectively improved.

Techniques are described for fast large file truncation. An example method includes receiving, by a data platform, a request to truncate a file, wherein data of the file is stored in a tree data structure including a plurality of nodes with leaf nodes corresponding to data of the file; determining a first node of the plurality of nodes including a plurality of child nodes; determining, based on a maximum key, a subset of the child nodes corresponding to a portion of the data that is to be retained; based on determining not to traverse the first node, updating a second node that is a parent of the first node to include a pointer to each child node of the subset of child nodes; and moving, from a subset of leaf nodes referenced by the subset of child nodes, a leaf node to rebalance the tree data structure.

A storage node may organize a database by storing current data in a balanced tree structure and storing prior data in a log structure. The storage node may update the database by replacing old data in the balanced tree with new data from a write and writing the old data to the log structure. The storage node may be able to locate appropriate date to respond to time-specific queries using the balanced tree structure and the log, and may be able to locate and remove old data with minimal computational resources.

The invention discloses a molecular system processing method and device related to molecular dynamics simulation, and the method comprises the steps: constructing a to-be-simulated molecular system through a Hierachy structure, reconstructing the Hierachy structure to obtain a balance tree Hierachy structure, carrying out the data updating of each basic element in a space unit of the balance tree Hierachy structure, and carrying out the data updating of the to-be-simulated molecular system through the data updating of each basic element in the space unit of the balance tree Hierachy structure. According to the method, data iteration is carried out on other space units at the same time until the top layer of the Hierarchy structure of the balance tree is completely updated, and the Hierarchy hierarchical structure and the actual molecular data file are connected through the index, so that the content of the unit or the space unit needing to be loaded is quickly found, the capacity of quick positioning is provided, and the operation efficiency is well improved.

The application relates to a time-effect balanced tree-based data processing method and system, and relates to a time-effect balanced tree-based data processing method and system which solves the problem that the node with the highest query frequency in a short period usually consumes a high cost on the balanced tree structure of a computer, the single query consumption is usually fixed as logarithmic level time, the balanced tree lacks sensitivity to the query frequency and the time-effectiveness of recent queries, and the query time is too long each time. The application constructs a time-effect balanced tree, stores data according to the structure of the time-effect balanced tree, and the empty tree, single node, left subtree and right subtree of the time-effect balanced tree are all time-effect balanced trees. Each node on the time-effect balanced tree comprises a retrieval key value pair, a time-effect weight and a record query round, and the time-effect weight is calculated by a decay method. The unbalance degree, balance state and rotation mode of the time-effect balanced tree are defined. The computer data processing is completed according to the definition, the time-effect balanced tree and the time-effect weight. The application belongs to the field of computer data processing.

Systems and methods for using a scalable collection within a balanced tree data structure are provided herein. In particular, techniques for generating scalable collections and using scalable collections for fetching data corresponding to an application using balanced chunked data tree structures are provided herein. In an example, the method may include determining a document defined by a balanced tree data structure having a root chunk, reference chunks, and chunks each having nodes each corresponding to a document attribute. The nodes may include a scalable collection containing a list node and child nodes associated with the list node, and a placeholder node that provides a reference to another chunk based on a position of the placeholder node within a respective chunk. The method may also include navigating to content within the document based on the balanced tree data structure.

The application discloses a data queue management method and system based on a balanced binary tree, relates to communication technology, and comprises the following steps: establishing a data node structure of a balanced binary tree; receiving or sending data through a queue according to task requirements, wherein, in the case that a communication node sends data outward, a corresponding sending queue is established based on the balanced binary tree; in the case that the communication node receives data, a data node is inserted into the balanced binary tree, and when the priority of data to be inserted is higher than that of a leaf node to be sent redundantly, the data is inserted after the corresponding leaf node; and after the insertion or deletion operation is completed, the balanced binary tree is balanced by rotating left or right according to a balance factor of the balanced binary tree. The method adjusts the priority of different data types according to different types of data, prevents the accumulation of the same data link table in the balanced binary tree, and improves the sending efficiency of the balanced binary tree queue.

This invention relates to the field of data indexing technology, and discloses a method, apparatus, device, and medium for constructing a dynamically balanced KD-tree index based on real-time streaming data. The method includes determining a target trajectory stream; determining the initial spatial range of the root node of the initial KD-tree based on the geographical range of the target trajectory stream; dividing the initial spatial range of the root node into two initial subspaces using the coordinates of the first point of interest as the dividing point; performing a dynamic insertion operation for each target point of interest reached after the first point of interest; triggering a split operation for the leaf node when the number of points of interest stored in any leaf node exceeds a first preset capacity threshold; and monitoring the comprehensive imbalance index of the initial KD-tree in real time, triggering a global reconstruction process when the comprehensive imbalance index value exceeds a preset reconstruction threshold. This solution improves the balance of the KD-tree and query efficiency.

The present invention relates to the field of data storage, and disclosed are a data reading method and a related apparatus. The method comprises: a server receives from a client a first processing request for instructing to acquire shard boundary information of target data, and returns the shard boundary information of the target data to the client in response to the first processing request and by scanning a target self-balancing tree corresponding to the target data; and the server receives from the client a second processing request for instructing to acquire P shards of the target data, and returns the P shards of the target data to the client in the form of shards in response to the second processing request. The shard boundary information of the target data comprises values of identifiers of the target data stored in a root node or child nodes of a root node of the target self-balancing tree. Values of identifiers of target data stored in a root node or child nodes of a root node are used as shard boundary information, thereby accelerating determination of the shard boundary information, and improving the efficiency of performing data reading on P shards of the target data.