Computer system and method for indexing and retrieval of partially specified type-less semi-infinite information

Abstract

A system for Partial Unstructured Information Processing, constituting storing, indexing, querying and retrieval of partially specified unstructured data, the system comprising: Quantum Clustering Algorithm that partitions data records in different dusters such that the data in each cluster can be indexed efficiently, a Compressed Ternary Tree that replaces all conceivable indices for each cluster thereby solving the Unthinkable Query Problem for each cluster, and a Virtual Query Processor that converts traditional data base queries to raw Compressed Ternary Tree queries and appropriate filters.

1 . A system for Partial Unstructured Information Processing, constituting storing, indexing, querying and retrieval of partially specified unstructured data, the system comprising: a Quantum Clustering Algorithm that partitions data records in different clusters such that the data in each cluster can be indexed efficiently, a Compressed Ternary Tree that replaces all conceivable indices for each cluster thereby solving the Unthinkable Query Problem for each cluster, and a Virtual Query Processor that converts traditional data base queries to raw Compressed Ternary Tree queries and appropriate filters. 
     
     
         2 . A method for Partial Unstructured Information Processing, constituting storing, indexing and retrieval of partially specified unstructured data, the method comprising: a Quantum Clustering Algorithm that partitions data records in different clusters wherein each cluster is associated with a quantum key, wherein keys, represented by semi-infinite ternary bit strings, that are added to a cluster are attached to the quantum key associated with the cluster and keys that are removed from a cluster are detached from the quantum key associated with the cluster, wherein a new key to be inserted is matched against the quantum key of each existing cluster, wherein the best match is compared to a threshold to determine if the match is sufficiently good, wherein the key to be inserted is added to the cluster with the best matching quantum key if the match is sufficiently good, wherein a new cluster is created followed by adding the key to be inserted to the new cluster if the best match is not sufficiently good and a Compressed Ternary Tree that replaces all conceivable indices for each cluster wherein each cluster is associated with a Compressed Ternary Tree, wherein the new key is inserted in the Compressed Ternary Tree of the selected cluster. 
     
     
         3 . The method according to  claim 2 , further comprising a Virtual Query Processor that converts traditional data base queries to raw Compressed Ternary Tree queries (raw query) and appropriate filters, wherein a raw query consists of start, length, pattern and negate, wherein the pattern of a raw query are either a ternary bit strings or a general integer intervals converted to intervals that can be represented using power-of-2-completion, wherein proper set operations such as union and intersection are used to combine) results from partitioned queries to produce the result of the original query, wherein intersection pruning is used to increase the speed for executing complex queries. 
     
     
         4 . The method of  claim 2 , further comprising a basic, ordered, lazy or replicating CTTs. 
     
     
         5 . The method according to  claim 4  wherein the criteria for selecting which nodes to lock in a replicating CTT is based on available metrics including depth, height, cost, density, weight, and frequency. 
     
     
         6 . The method according to  claim 2 , further comprising packed CTTs. 
     
     
         7 . The method according to  claim 6  wherein candidate selection in each packed CTT is based on available metrics including depth, height, cost, density, weight, and frequency. 
     
     
         8 . The method according to  claim 2 , further comprising compressed CTTs. 
     
     
         9 . The method according to  claim 9  wherein compression in a compressed CTT is achieved by pointer compression, structure compression or template compression. 
     
     
         10 . The method according to  claim 2  further comprising lookup of a query key by looking up the query key in the Compressed Ternary Tree of each cluster and computing the set union of the results. 
     
     
         11 . The method according to  claim 3  comprising Virtual Query Processor queries consisting of start, length, pattern and negate. 
     
     
         12 . The method according to  claim 11  wherein the pattern of a Virtual Query Processor query is either a ternary bit string or an integer interval. 
     
     
         13 . The method according to  claim 12  wherein power-of-2-completion is used to partition general integer intervals to intervals that can be represented by ternary bit strings. 
     
     
         14 . The method according to  claim 3  wherein proper set operations such as union and intersection are used to combine results from partitioned queries to produce the result of the original query. 
     
     
         15 . The method according to  claim 3  wherein intersection pruning is used to increase the speed for executing complex queries. 
     
     
         16 . A computer program loadable into a memory communicatively connected or coupled to at least one data processor, comprising software for executing the method according to  claim 2  when the program is run on the at least one data processor. 
     
     
         17 . A processor-readable medium, having a program recorded thereon, where the program is to make at least one data processor execute the method according to  claim 2  when the program is loaded into the at least one data processor.