Abstract: A data relationships storage platform for analysis of one or more data sources is described herein. A data processing system may be communicatively coupled to one or more data sources and one or more big-data databases. One or more collectors may collect data pieces from the one or more data sources. One or more analyzer may analyze the collected data pieces to determine whether one or more relationships exist between the collected data pieces. The analysis results in one or more data globs that include one or more of the data pieces and relationship information, such as tags. The tagged data globs may be communicated to and stored in one or more big-data databases.

Abstract: Usage tracking NFTs (UT-NFTs) provide a protocol and an exchange for scalable content distribution and tracking. The protocol provides programmable UT-NFTs that can link multiple layers of content. As the UT-NFTs are used, the content providers are paid via a cyptocurrency exchange. The exchange dictates and controls the dynamic pricing of content according to how instrumental the content is to the activities of the end user.

Abstract: A data relationships storage platform for analysis of one or more data sources is described herein. A data processing system may be communicatively coupled to one or more data sources and one or more big-data databases. One or more collectors may collect data pieces from the one or more data sources. One or more analyzer may analyze the collected data pieces to determine whether one or more relationships exist between the collected data pieces. The analysis results in one or more data globs that include one or more of the data pieces and relationship information, such as tags. The tagged data globs may be communicated to and stored in one or more big-data databases.

Abstract: A computer-implemented method performed at a server system having one or more processors and memory, the method comprising receiving a set of curated documents comprising one or more documents identified as being relevant to a sector, analyzing the set of curated documents to determine one or more words and a count of each of the one or more words for all documents of the curated set of documents, further analyzing the set of curated documents, by analyzing one or more n-grams based on the one or more words, determining a first score based on a term frequency and a global document frequency of each of the one or more words of each of the one or more n-grams, determining a document vector based on averages of the first score, where the document vector comprises a perfect document for the sector, and storing the document vector in the data store.

Abstract: A computer-implemented method performed at a server system having one or more processors and memory, the method comprising receiving a set of curated documents comprising one or more documents identified as being relevant to a sector, analyzing the set of curated documents to determine one or more words and a count of each of the one or more words for all documents of the curated set of documents, further analyzing the set of curated documents, by analyzing one or more n-grams based on the one or more words, determining a first score based on a term frequency and a global document frequency of each of the one or more words of each of the one or more n-grams, determining a document vector based on averages of the first score, where the document vector comprises a perfect document for the sector, and storing the document vector in the data store.

Abstract: An enterprise data processing module and method are described herein. The enterprise data processing module comprises at least one collector and at least one analyzer. The collectors may be operable to collect data pieces from a plurality of data sources. The analyzers may be operable to analyze the collected data pieces to determine cross-source relationships that exist between the data pieces collected from the plurality of sources. The analyzed data pieces may be stored in one or more big-data databases as blocks of data according to the cross-source relationships.

Abstract: An enterprise data processing module and method are described herein. The enterprise data processing module comprises at least one collector and at least one analyzer. The collectors may be operable to collect data pieces from a plurality of data sources. The analyzers may be operable to analyze the collected data pieces to determine cross-source relationships that exist between the data pieces collected from the plurality of sources. The analyzed data pieces may be stored in one or more big-data databases as blocks of data according to the cross-source relationships.

Abstract: A data relationships storage platform for analysis of one or more data sources is described herein. A data processing system may be communicatively coupled to one or more data sources and one or more big-data databases. One or more collectors may collect data pieces from the one or more data sources. One or more analyzer may analyze the collected data pieces to determine whether one or more relationships exist between the collected data pieces. The analysis results in one or more data globs that include one or more of the data pieces and relationship information, such as tags. The tagged data globs may be communicated to and stored in one or more big-data databases.

Abstract: A data relationships storage platform for analysis of one or more data sources is described herein. A data processing system may be communicatively coupled to one or more data sources and one or more big-data databases. One or more collectors may collect data pieces from the one or more data sources. One or more analyzer may analyze the collected data pieces to determine whether one or more relationships exist between the collected data pieces. The analysis results in one or more data globs that include one or more of the data pieces and relationship information, such as tags. The tagged data globs may be communicated to and stored in one or more big-data databases.

Abstract: Systems and methods are provided for data analysis that may be initialized via self-identification from customers and continually trained automatically thereafter. A plurality of records are partitioned into a plurality of tagged sets. The plurality of tagged sets comprises a positive set, a negative set, and a neutral set. A model is generated according to a first portion of the plurality of tagged sets. Then, an initial fit of the model is evaluated according to a second portion of the plurality of tagged sets. The model may then be adjusted according to the initial fit of the model. A final fit of the adjusted model is evaluated according to a third portion of the plurality of tagged sets.

Abstract: Systems and methods are provided for determining a sentiment score for an article, tagging the article with an entity and one or more of a plurality of signals, determining a daily sentiment score according the entity, and determining an average sentiment score according the daily sentiment score and a predetermined time period. System and methods are provided for assessing components of Growth and Risk calculations via sub-indices and their resulting growth and risk scores at an entity or industry level.

Abstract: A computer-implemented method performed at a server system having one or more processors and memory, the method comprising receiving a set of curated documents comprising one or more documents identified as being relevant to a sector, analyzing the set of curated documents to determine one or more words and a count of each of the one or more words for all documents of the curated set of documents, further analyzing the set of curated documents, by analyzing one or more n-grams based on the one or more words, determining a first score based on a term frequency and a global document frequency of each of the one or more words of each of the one or more n-grams, determining a document vector based on averages of the first score, where the document vector comprises a perfect document for the sector, and storing the document vector in the data store.

Abstract: A computer-implemented method performed at a server system having one or more processors and memory, the method comprising receiving a set of curated documents comprising one or more documents identified as being relevant to a sector, analyzing the set of curated documents to determine one or more words and a count of each of the one or more words for all documents of the curated set of documents, further analyzing the set of curated documents, by analyzing one or more n-grams based on the one or more words, determining a first score based on a term frequency and a global document frequency of each of the one or more words of each of the one or more n-grams, determining a document vector based on averages of the first score, where the document vector comprises a perfect document for the sector, and storing the document vector in the data store.

Abstract: A data relationships storage platform for analysis of one or more data sources is described herein. A data processing system may be communicatively coupled to one or more data sources and one or more big-data databases. One or more collectors may collect data pieces from the one or more data sources. One or more analyzer may analyze the collected data pieces to determine whether one or more relationships exist between the collected data pieces. The analysis results in one or more data globs that include one or more of the data pieces and relationship information, such as tags. The tagged data globs may be communicated to and stored in one or more big-data databases.

Abstract: A data relationships storage platform for analysis of one or more data sources is described herein. A data processing system may be communicatively coupled to one or more data sources and one or more big-data databases. One or more collectors may collect data pieces from the one or more data sources. One or more analyzer may analyze the collected data pieces to determine whether one or more relationships exist between the collected data pieces. The analysis results in one or more data globs that include one or more of the data pieces and relationship information, such as tags. The tagged data globs may be communicated to and stored in one or more big-data databases.

Abstract: A computer-implemented method performed at a server system having one or more processors and memory, the method comprising receiving a set of curated documents comprising one or more documents identified as being relevant to a sector, analyzing the set of curated documents to determine one or more words and a count of each of the one or more words for all documents of the curated set of documents, further analyzing the set of curated documents, by analyzing one or more n-grams based on the one or more words, determining a first score based on a term frequency and a global document frequency of each of the one or more words of each of the one or more n-grams, determining a document vector based on averages of the first score, where the document vector comprises a perfect document for the sector, and storing the document vector in the data store.

Abstract: An enterprise data processing module and method are described herein. The enterprise data processing module comprises at least one collector and at least one analyzer. The collectors may be operable to collect data pieces from a plurality of data sources. The analyzers may be operable to analyze the collected data pieces to determine cross-source relationships that exist between the data pieces collected from the plurality of sources. The analyzed data pieces may be stored in one or more big-data databases as blocks of data according to the cross-source relationships.

Abstract: An enterprise data processing module and method are described herein. The enterprise data processing module comprises at least one collector and at least one analyzer. The collectors may be operable to collect data pieces from a plurality of data sources. The analyzers may be operable to analyze the collected data pieces to determine cross-source relationships that exist between the data pieces collected from the plurality of sources. The analyzed data pieces may be stored in one or more big-data databases as blocks of data according to the cross-source relationships.

Abstract: A computer-implemented method performed at a server system having one or more processors and memory, the method comprising receiving a set of curated documents comprising one or more documents identified as being relevant to a sector, analyzing the set of curated documents to determine one or more words and a count of each of the one or more words for all documents of the curated set of documents, further analyzing the set of curated documents, by analyzing one or more n-grams based on the one or more words, determining a first score based on a term frequency and a global document frequency of each of the one or more words of each of the one or more n-grams, determining a document vector based on averages of the first score, where the document vector comprises a perfect document for the sector, and storing the document vector in the data store.

Abstract: A data relationships storage platform for analysis of one or more data sources is described herein. A data processing system may be communicatively coupled to one or more data sources and one or more big-data databases. One or more collectors may collect data pieces from the one or more data sources. One or more analyzer may analyze the collected data pieces to determine whether one or more relationships exist between the collected data pieces. The analysis results in one or more data globs that include one or more of the data pieces and relationship information, such as tags. The tagged data globs may be communicated to and stored in one or more big-data databases.