Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
  • G06F16/22—Indexing; Data structures therefor; Storage structures
  • G06F16/2228—Indexing structures
  • G06F16/2255—Hash tables
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
  • G06F16/22—Indexing; Data structures therefor; Storage structures
  • G06F16/2228—Indexing structures
  • G06F16/2237—Vectors, bitmaps or matrices
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
  • G06F16/22—Indexing; Data structures therefor; Storage structures
  • G06F16/2228—Indexing structures
  • G06F16/2246—Trees, e.g. B+trees
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
  • G06F16/24—Querying
  • G06F16/245—Query processing
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/90—Details of database functions independent of the retrieved data types
  • G06F16/901—Indexing; Data structures therefor; Storage structures
  • G06F16/9024—Graphs; Linked lists

Definitions

• FIGS. 3-3D depict working examples in accordance with aspects of the disclosure.
• FIG 4 is another example flow diagram in accordance with aspects of the present disclosure.
• an apparatus may comprise a memory and at least one processor configured to execute the following operations: generate an index in the memory, the index comprising a hierarchy of interlinked data structures and a hash table such that an entry in the hash table comprises an association between a date and a first data structure in the hierarchy of interlinked data structures; receive a search request from a remote device, the search request comprising a plurality of search parameters including the date; search the hash table, using the date, for a memory address of the first data structure in the hierarchy of interlinked data structures; determine a plan for traversing the hierarchy of interlinked data structures based on the search parameters; begin a search for data sought after by the search request at the first data structure in accordance with the traversal plan; and return results of the search to the remote device.
• an apparatus may comprise a memory, a display device, and at least one processor to execute the following operations: render a graphical user interface on the display device; transmit a search request to a remote server; receive a plurality of records in response to the search request, wherein a format of each received record matches a format of each memory space allocated for each record; copy each received record to a respective allocated memory space; add each allocated memory space to a linked list of memory spaces such that an order of the linked list reflects the order in which the plurality of records are received; and periodically render currently received records on the graphical user interface until all data records responsive to the search request are received.
• memory 104 of computer apparatus 101 may also store index
• index 103 may be a database organized in a specific manner to optimize queries for data occurring on a specific date.
• memory 104 may be a random access memory (“RAM”) device.
• memory 104 may be divided into multiple memory segments organized as dual in-line memory modules (“DIMMs”).
• DIMMs dual in-line memory modules
• memory 104 may comprise other types of devices, such as non-transitory computer readable media.
• Non-transitory computer readable media may comprise any one of many physical media (e.g., electronic, magnetic, optical, electromagnetic, or semiconductor media).
• non-transitory computer- readable media include, but are not limited to, a portable magnetic computer diskette such as floppy diskettes or hard drives, a read-only memory (“ROM”), an erasable programmable read-only memory, a portable compact disc or other storage devices that may be coupled to a computer apparatus directly or indirectly.
• Memory 104 may also include any combination of one or more of the foregoing and/or other devices as well.
• the instructions residing in memory 104 may be retrieved and executed by processor 102. These instructions may comprise any set of instructions to be executed directly (such as machine code) or indirectly (such as scripts) by processor 102.
• the terms "instructions,” “scripts,” or “modules” may be used interchangeably herein.
• the computer executable instructions may be stored in any computer language or format, such as in object code or modules of source code.
• the instructions may be implemented in the form of hardware, software, or a combination of hardware and software and that the examples herein are merely illustrative.
• At least one processor may generate a hash table in the memory.
• the hash table may be another aspect of the index.
• each entry in the hash table may comprise an association between a date and a first data structure of a hierarchy of interlinked data structures.
• FIG. 3 an illustrative hash table 302 is shown.
• Hash table 302 shows an association between date 304 ( i.e ., July 20, 2017) and data structure 308 and date 306 ⁇ i.e., February 2, 2016) with data structure 310.
• data structure 308 and data structure 310 are binary search trees (“BST”).
• At least one processor may receive a search request from a remote device, as shown in block 206.
• the search request may include a date and a plurality of other search parameters.
• at least one processor may search the hash table (e.g., hash table 302), using the date, for an associated memory address of a first data structure of a hierarchy of data structures.
• at least one processor of a computer may determine a plan for traversing the hierarchy of interlinked data structures based on the search parameters. In one example, determining a traversal plan may include determining a depth in the hierarchy where the sought after data may be found based on the search parameters.
• At least one processor may begin a search for the sought after data at the first data structure of the hierarchy, as shown in block 212.
• the BST 308 may be a first data structure in a hierarchy of data structures in this example.
• Each node in the BST shown in FIG. 3A may contain a pointer to a second data structure in the hierarchy.
• the first level of the hierarchy may contain the symbols while the second level of the hierarchy may contain the transactions associated with that symbol on that particular day.
• FIG. 3A In the example of FIG.
• node 312 contains a pointer to data structure 312A containing all the transactions for symbol“MMM” on July 20, 2017;
• node 320 contains a pointer to data structure 320A containing all the transactions for symbol“TTT” on July 20, 2017;
• node 314 contains a pointer to data structure 314A containing all the transactions for symbol“GGG” on July 20, 2017;
• node 316 contains a pointer to data structure 316A containing all the transactions for symbol“CCC” on July 20, 2017;
• node 318 contains a pointer to data structure 318A containing all the transactions for symbol“JJJ” on July 20, 2017;
• node 322 contains a pointer to data structure 322A containing all the transactions for symbol“PPP” on July 20, 2017; and
• node 324 contains a pointer to data structure 324A containing all the transactions for symbol “YYY” on July 20, 2017.
• the example of FIG. 3A contains only two levels of data, but it is understood that the hierarchy
• FIG. 3B a close up illustration of the data structure 320A is shown.
• a processor may arrive at node 320 of the BST in its search for“TTT” transactions on July 20, 2017.
• the first level data structure 308 only contains the symbols.
• the transactions may be found in the next level.
• data structure 320A is at a second level of the hierarchy and contains all the transactions for symbol“TTT” on July 20, 2017.
• data structure 320A may be a vector data structure.
• the vector data structure of FIG. 3B shows three nodes 326, 328, and 330 representing orders for symbol“TTT” on July 20, 2017.
• Each node contains a further linked list branching out therefrom that represents transactions for each order.
• node 326 may represent an order with two transactions 326A and 326B branching out therefrom;
• node 328 may represent an order with three transactions 328A, 328B, and 328C branching out therefrom; and
• node 330 may represent an order with one transaction 330A branching out therefrom.
• the results of the query may be returned, as shown in block 214.
• the data in vector data structure 320A may be sent back to the device requesting this information. While this example uses a vector data structure for the orders, it is understood that other types of data structures may be used in other situations, such as arrays, linked lists, or the like.
• node 332 contains symbol“MMM” and a pointer to data structure 332A containing customers transacting with symbol “MMM;” in turn, data structure 332A contains a pointer to a transactions data structure 332B indicative of transactions for symbol“MMM” by the customer.
• Node 344 contains symbol “GGG” and a pointer to data structure 344A containing customers transacting with symbol“GGG;” in turn, data structure 344A contains a pointer to a transactions data structure 344B indicative of transactions for symbol “GGG” by the customer.
• FIG. 3D a close up illustration of customers data structure 334A is shown.
• the“TTT” symbol node 334 is associated with data structure 334A by way of a pointer.
• the data structure 334A may also be arranged as a BST with root node 352 containing customer“JPMorgan;” node 354 containing customer“Chase;” and node 356 containing customer“NFSC.”
• FIG. 3C also depicts each customer node containing a respective pointer to a transactions data structure.
• GUI 510 with trading data query results is shown.
• the GUI comprises fields that may be typical of trading data including, but not limited to, status, symbol, price, size, etc.
• at least one processor of the client device may detect a user selection of a given column displayed on the GUI. In response to the selection, the processor may display aggregate data corresponding to the given column. By way of example, a user may click on the symbol (“Sym”) column of GUI 510. As a result, at least one processor may render another GUI 514, as shown in FIG. 5B, that aggregates all the orders related to the symbol column.
• At least one processor may do a linear scan of the linked list (e.g ., linked list 504) using the symbol column as the key in response to the user clicking on the symbol column. That is, a user may click on any column and at least one processor may us the selected column as a key for the linear scan.

Landscapes

• Engineering & Computer Science (AREA)
• Theoretical Computer Science (AREA)
• Databases & Information Systems (AREA)
• Data Mining & Analysis (AREA)
• Physics & Mathematics (AREA)
• General Engineering & Computer Science (AREA)
• General Physics & Mathematics (AREA)
• Software Systems (AREA)
• Computational Linguistics (AREA)
• Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
• User Interface Of Digital Computer (AREA)

Priority Applications (6)

Applications Claiming Priority (2)

Publications (1)

Family

ID=69059909

Family Applications (1)

Country Status (6)

Families Citing this family (2)

Citations (6)

Family Cites Families (49)

• 2018
  • 2018-07-06 US US16/028,641 patent/US11216432B2/en active Active
• 2019
  • 2019-07-02 CA CA3106389A patent/CA3106389A1/en active Pending
  • 2019-07-02 JP JP2021500226A patent/JP2021532450A/ja active Pending
  • 2019-07-02 EP EP19830168.1A patent/EP3818451B1/en active Active
  • 2019-07-02 SG SG11202100056SA patent/SG11202100056SA/en unknown
  • 2019-07-02 WO PCT/US2019/040352 patent/WO2020010115A1/en not_active Ceased
• 2022
  • 2022-01-03 US US17/567,230 patent/US11954086B2/en active Active
• 2023
  • 2023-10-25 JP JP2023183465A patent/JP7781835B2/ja active Active
• 2024
  • 2024-03-11 US US18/601,480 patent/US12353388B2/en active Active
• 2025
  • 2025-06-10 US US19/233,000 patent/US20250307224A1/en active Pending
  • 2025-09-18 JP JP2025154611A patent/JP2025178356A/ja active Pending

Patent Citations (6)

Also Published As

Similar Documents

Legal Events