A system and method for determining root causes of a failure in a virtual desktop system in real time is disclosed. The virtual remote desktop system has interconnected service components to make a virtual desktop available on a client device, and agents monitoring events from the components. An event collection module is coupled to the agents of the desktop system. The event collection module compiles events from the components relating to the failure. An aggregation module is coupled the event collection module. The aggregation module correlates events in a time-ordered stream relating to the fault. A diagnosis analysis system identifies one of the events of the time-ordered stream as the likely root cause of the fault.

Provided is an analysis device with which it is possible to find information relating to the intention and purpose of an attacker. The analysis device is provided with a purpose estimating means that estimates the purpose of behavior, based on predetermined behavior in the computer and knowledge information that includes the relation between the behavior and the purpose of executing the behavior.

A method includes storing, by one or more processors of one or more computing devices, a plurality of datasets in a non-transitory computer memory associated with the one or more computing devices. A plurality of index representations is generated where each one of the plurality of index representations includes a compressed representation of a respective one of the plurality of datasets. The plurality of index representations is stored in the non-transitory computer memory. A sample dataset is received by the one or more processors. A sample dataset representation is generated that includes a compressed representation of the sample dataset. A determination that at least one of the plurality of datasets is most similar to the sample dataset based on the sample dataset representation and the plurality of index representations is performed.

A system for measuring similarity between a binary query vector and a plurality of binary candidate vectors includes a storage unit and a processor. The storage unit stores the binary query vector and the plurality of candidate vectors, and the processor performs Tanimoto calculations in terms of Hamming distances. The processor includes a Tanimoto to Hamming threshold converter, a Hamming measurer, and a Hamming comparator. The Tanimoto to Hamming threshold converter converts a Tanimoto threshold into a Hamming threshold. The Hamming measurer measures the Hamming distances between the candidate vectors and the query vector. The Hamming comparator selects candidate vectors whose Hamming distance from the query vector is less than or equal to the Hamming threshold.

The system described herein can automatically match, link, or otherwise associate electronic activities with one or more record objects. For an electronic activity that is eligible or qualifies to be matched with one or more record objects, the system can identify one or more set of rules or rule sets. Using the rule sets, the system can identify candidate record objects. The system can then rank the identified candidate record objects to select one or more record objects with which to associate the electronic activity. The system can then store an association between the electronic activity and the selected one or more record objects.

Strategic competitive intelligence data generation systems and methods are provided. The system stores internal intellectual property data of an entity, receives external technology development data from predefined sources and intellectual property data from the internal portfolio database of the entity, stores a set of relevant rules, executes them, filters the technology development data and the intellectual property data, based on the rules executed by the rule engine. It stores in one of an entity aligned cluster database, the filtered technology development data and the intellectual property data. The entity aligned cluster databases are created against predefined entity aligned clusters. The system executes an input query, determines one relevant entity aligned cluster database for executing the search query, and populates relevancy scores in two matrices, aggregates the relevancy scores through multiplication of the matrices. The system then analyses the resultant matrix based on set of rules and displays results of the analytics.

Provided are systems for conducting private set intersection (PSI) techniques with multiple parties using a data repository that include at least one processor to generate a data repository, receive, from a submission entity system associated with a submission entity, a private set intersection (PSI) data query that includes a match parameter for performing the PSI data query, transmit, to the submission entity system, a data classification encryption key, wherein the data classification encryption key is associated with a data field that corresponds to a match parameter data field of the match parameter, determine whether to authorize the PSI data query on the data repository, transmit, to the submission entity system, a data authorization encryption key based on determining to authorize the PSI data query on the data repository, and perform the PSI data query on the data repository. Methods and computer program products are also provided.

Computer system compares data across two data sources. Database connectivity drivers access the first and second data sources. RAM receives and stores data from each of the first and second data sources accessed by the drivers. The data from the first data source are stored in a first table in the RAM and data from the second data source are stored in a second table in the RAM. A processor compares data in the first and second tables. The processor is programmed to compare the data by identifying a set of primary keys across the first and second tables. The set of primary keys are a set of one or more columns in the both first and second tables that uniquely identify rows across both the first and second tables. The processor then compares the data in the first and second tables, which comparison can include: determining, based on the identified set of primary keys, a total number of mismatched cell values across the first and second tables; and computing a score for the comparison of the first and second tables, where the score is computed based on the total number of mismatched cell values. The computer system can then generate an electronic comparison report based on the comparing of the first table and second table.

Described herein is a computer implemented method for inserting a new item to a tree-structured dataset. The method comprises: calculating a new item data summary; generating a new leaf node for storing the new item and the new item data summary; adding the new leaf node to the dataset; and recalculating data summaries for all internal dataset nodes in an update path starting at a parent of the new leaf node and ending at a root node of the dataset. The data summary for a given internal node in the update path is calculated based on data summaries for each of the given internal node's children nodes.

A computer-implemented method, comprising receiving an order associated with a user, and capturing information associated with a physical attribute of the user; for the order being completed, performing a matching operation on the physical attribute of the user and information associated with a region of the user that includes the physical attribute of the user and respective physical attributes of other users, to determine a closest match; and generating an output that identifies the user associated with the closest match in the information associated with the region of the user, and providing the output to a server to deliver the order to the user.