Systems and methods for dynamic data processing and graphical user interface generation are provided. A system may include a network interface configured to request and receive, via a computer network from one or more sources in remote locations, electronic record data associated with an individual; an input filter configured to identify structured and unstructured information in the electronic record data; a data selector configured to analyze the structured and unstructured information; a timeline generator configured to generate, based on the analysis, interface information for displaying an interactive graphical user interface configured to present an event timeline of events in the electronic record data; and a display configured to provide the interactive graphical user interface based on the generated interface information.

Systems and methods for dynamic data processing and graphical user interface generation are provided. A system may include a network interface configured to request and receive, via a computer network from one or more sources in remote locations, electronic record data associated with an individual; an input filter configured to identify structured and unstructured information in the electronic record data; a data selector configured to analyze the structured and unstructured information; a timeline generator configured to generate, based on the analysis, interface information for displaying an interactive graphical user interface configured to present an event timeline of events in the electronic record data; and a display configured to provide the interactive graphical user interface based on the generated interface information.

Methods, systems, and computer-readable media for indexing partitions using distributed Bloom filters are disclosed. A data indexing system generates a plurality of indices for a plurality of partitions in a distributed object store. The indices comprise a plurality of Bloom filters. An individual one of the Bloom filters corresponds to one or more fields of an individual one of the partitions. Using the Bloom filters, the data indexing system determines a first portion of the partitions that possibly comprise a value and a second portion of the partitions that do not comprise the value. Based (at least in part) on a scan of the first portion of the partitions and not the second portion of the partitions, the data indexing system determines one or more partitions of the first portion of the partitions that comprise the value.

A search query is received from a search engine of multiple search engines. One or more search results from each search engine of the multiple search engines in response to the search query are received. Each search result of the one or more search results includes a first search score. A factor is determined for each search result of the one or more search results. A second search score is determined for each search result of the one or more search results based on the first search score and the factor. The one or more search results are displayed to a user of the search engine in an order. The order is based on the second search score.

A data analysis system determines characteristics of a data set such as statistical measures, analytical insights, data trends, or relationships with other data sets. The system determines a level of importance for each determined characteristic using metadata associated with the data set, and, in some cases, user preferences provided by the user. Such metadata may include descriptive names, data types, and data characteristics of the data set and of data elements within the data set.

An embodiment of the present invention is directed to a backup validation engine. The backup validation engine comprises: a plurality of data sources comprising a central asset repository; a central repository of backups data; a central repository of network attached storage, a central application portfolio repository; and central repository of tape backup data; an interface user interface; and a data mapping processor, coupled to the plurality of data sources and interactive user interface, programmed to: access data from each of the plurality of data sources; map data from the plurality of data sources; identify performance issues comprising: duplicate host policies, hosts back-up, missing backup of hosts, duplicate NAS policies, NAS volume backup, and missing NAS backup; generate a backup validation plan to address one or more performance issues; and automatically initiate the backup validation plan.

An example operation may include one or more of receive a document identifier (ID), retrieve a document from a storage based on the ID, generate a hash of the document, and compare the hash of the document under the ID against a hash of the document stored on the blockchain for verification.

The subject matter described herein provides techniques to ensure that queries of a distributed database observe a consistent read of the database without locking or logging. In this regard, next-write timestamps uniquely identify a set of write transactions whose updates can be observed by reads. By publishing the next-write timestamps from within an extendable time lease and tracking a “safe timestamp,” the database queries can be executed without logging read operations or blocking future write transactions, and clients issuing the queries at the “safe timestamp” observe a consistent view of the database as it exists on or before that timestamp. Aspects of this disclosure also provide for extensions, done cheaply and without the need for logging, to the range of timestamps at which read transactions can be executed.

The subject matter described herein provides techniques to ensure that queries of a distributed database observe a consistent read of the database without locking or logging. In this regard, next-write timestamps uniquely identify a set of write transactions whose updates can be observed by reads. By publishing the next-write timestamps from within an extendable time lease and tracking a “safe timestamp,” the database queries can be executed without logging read operations or blocking future write transactions, and clients issuing the queries at the “safe timestamp” observe a consistent view of the database as it exists on or before that timestamp. Aspects of this disclosure also provide for extensions, done cheaply and without the need for logging, to the range of timestamps at which read transactions can be executed.

Techniques are described for generating an approximate frequency histogram using a series of Bloom filters (BF). For example, to estimate the f1 and f2 cardinalities in a dataset, an ordered chain of three BFs is established (“BF1”, “BF2”, and “BF3”). An insertion operation is performed for each datum in the dataset, whereby the BFs are tested in order (starting at BF1) for the datum. If the datum is represented in a currently-tested BF, the subsequent BF in the chain is tested for the datum. If the datum is not represented in the currently-tested BF, the datum is added to the BF, a counter for the BF is incremented, and the insertion operation for the current datum ends. To estimate the cardinality of f1-values in the dataset, the BF2-counter is subtracted from the BF1-counter. Similarly, to estimate the cardinality of f2-values in the dataset, the BF3-counter is subtracted from the BF2-counter.