Abstract: Systems, methods, and software described herein generate and manage a compressed database using a dictionary and a Huffman data structure. In one implementation, a method includes determining a frequency that each string of a plurality of strings in a segment of a database appears in the segment. The method further provides for identifying a first subset of strings with the highest frequency of appearance in the data structure and generating a dictionary for the first subset of strings. The method also includes generating a Huffman data structure based on the frequencies associated with the plurality of strings and generating a compressed segment based on the dictionary and the Huffman data structure.

Abstract: Graph capabilities are embedded as a service (e.g., a software as a service (SAAS)) into relational databases or other databases so that graph analytics can be realized and relationships can be analyzed. A seamless graph analytics experience, using a graph analytics system, is provided to a database platform. Advantages include security and advance graph analytics can be run on data in database platforms (e.g., relational databases, kv-stores, etc.) The database platform console can be used to instruct the graph analytics to be performed on the data in the database platform. The insights and results from the graph analytics may be stored in the database platform as tables and can be joined with other data of the database platform and/or used in machine learning. A user of the database platform does not have to leave that environment to perform graph analytics on the data of the database platform.

Abstract: Query processing using online transactional processing (OLTP) and/or online analytical processing (OLAP) use a distributed graph. Optimizations are described including compute pushdown OLTP mode, single thread OLTP mode, and automatic transition between OLTP mode and OLAP mode. A query processing engine comprises an OLTP processing engine, an OLAP processing engine, and a storage (e.g., a computer memory or storage). The OLTP processing engine is configured to process OLTP queries, and the OLAP processing engine is configured to process OLAP queries. The query processing engine may comprise a transition determination engine configured to determine when to transition query processing between OLTP query processing mode and OLAP query processing mode.

Abstract: Systems, methods, and software described herein manage logs and cache storage for a graph database. In one implementation, a replica in a cluster of replicas, identifies an update associated with a graph database. In response to the update, the first replica determines whether a quorum exists for the update using one or more additional replicas and, when a quorum exists, generates an entry in a log based on the update.

Abstract: Systems, methods, and software described herein manage graph query response using bloom filters and edge indexes. In one implementation, a graph management system may generate indexes and delta indexes for edges in a graph. Once generated, the graph management system may receive a query to the graph and respond to the query using the index and at least one delta index of the delta indexes.

Abstract: Systems, methods, and software described herein manage and use a secondary index associated with graph vertices. In one example, a graph management system may generate an index for vertices in a graph associated with an attribute type, wherein each of the vertices include a unique identifier and one or more attributes, and wherein each entry in the index associates at least one attribute value with one or more of the vertices with an attribute that qualifies for the entry. The graph management system further generates one or more delta indexes associated with the index based on modifications to the graph and generates query responses using the index and at least one of the one or more delta indexes.

Abstract: A database search method combines SQL command, complex event processing, and keyword search. Event data records may be collected based on the events specified in a query. A complex event processing analysis is used to determine the records that meet the correct sequence of events. The keyword search is used to filter user data records that have the attributes specified in the query. A SQL command may be used to further analyze or group the data records found. One or more metrics of the filtered results are presented as the result of the query. To run a new query efficiently, user identifiers are distributed using hashes. Adaptive sampling is used to determine whether the search result is within a desired margin of error. An efficient search for a specific condition is performed using a pace computation that jumps into a location of user data records that have the attributes.

Abstract: A generic approach for automatically ingesting data into an HDFS (Hadoop File System) based data warehouse includes a datahub server, a generic pipelined data loading framework, and a meta-data model that, together, address data loading efficiency, data source heterogeneities, and data warehouse schema evolvement. The loading efficiency is achieved via the MapReduce scale-out solution. The meta-data model is comprised of configuration files and a catalog. The configuration file is setup per ingestion task. The catalog manages the data warehouse schema. When a scheduled data loading task is executed, the configuration files and the catalog collaboratively drive the datahub server to load the heterogeneous data to their destination schemas automatically.

Abstract: A method and apparatus for a data warehouse built on top of MapReduce is provided. A virtual view is defined on top of the common star or snowflake data warehouse schema. A SQL-like, but much more succinct, query language is provided to leverage the virtual view. Many advanced query processing features leveraging the virtual view are also provided. Further, several optimization techniques ranging from data compression and access method to multi-query optimization and exploiting materialized views are provided. Further, a technique to seamlessly integrate the data warehouse system into any ad-hoc MapReduce jobs is provided. Thus, fully leveraging the power of both MapReduce and data warehouse technologies is achieved.

Abstract: A data management apparatus for digital advertising includes a data integration processor for collecting and storing data from providers, resolving heterogeneity of the data at schema and data levels, and performing validity checks of the data; an analytics processor for receiving validated data from the data integration processor and providing to users custom, nesting-aware, SQL-like query language and a library of data mining methods, machine learning models, and analytical user profiles (AUP); and an activation processor for encapsulating complex computations performed in real-time, segment evaluation, and online user classification using runtime user profiles (RUP).

Abstract: A generic approach for automatically ingesting data into an HDFS (Hadoop File System) based data warehouse includes a datahub server, a generic pipelined data loading framework, and a meta-data model that, together, address data loading efficiency, data source heterogeneities, and data warehouse schema evolvement. The loading efficiency is achieved via the MapReduce scale-out solution. The meta-data model is comprised of configuration files and a catalog. The configuration file is setup per ingestion task. The catalog manages the data warehouse schema. When a scheduled data loading task is executed, the configuration files and the catalog collaboratively drive the datahub server to load the heterogeneous data to their destination schemas automatically.

Abstract: A unified data management platform creates audience segments by combining proprietary and third party data, assists in determining what data to buy and how to manage all aspects of third party purchased data, controls data permissions by client, tracks data utilization, and attributes and reports data cost. The platform provides solutions that address how to leverage custom audience segments across multiple demand side platforms (DSPs) and multiple media channels, such as display, video, mobile, digital TV, and digital-out-of-home, and provides approaches that allow management of all aspects of Internet advertising from a custom domain.

Abstract: Systems and method are disclosed for applying a query to an event stream by storing one or more event constraints; performing constraint aware complex event processing on the query and the event constraints; and optimizing the query at run time.

Abstract: Systems and method are disclosed for applying a query to an event stream by storing one or more event constraints; performing constraint aware complex event processing on the query and the event constraints; and optimizing the query at run time.

Abstract: Systems and methods process generalized-tree-pattern queries by processing a twig query with a bottom-up computation to generate a generalized tree pattern result; encoding the generalized tree pattern results using hierarchical stacks; enumerating the generalized tree pattern result with a top-down computation; a hybrid of top-down and bottom-up computation for early result enumeration before reaching the end of document; and a more succinct encoding scheme that replaces the hierarchical stacks to further improve the performance.

Abstract: Systems and methods to provide two bottom up path matching solutions and one post processing solution for evaluating value predicates and tree pattern queries. The first path matching method triggers the matching whenever a leaf query step is seen and stores the prefix sub-matches in a cache for reuse. The second path matching method is an NFA (non-deterministic finite state automata) based solution through a post-order traversal of the XML document tree. The post processing method relies on a compact encoding the path results, which avoids redundant value predicate, join evaluations and any duplicate elimination, sort and grouping operations.

Abstract: Systems and methods are disclosed to guarantee the safety of a continuous join query (CJQ) over one or more punctuated data streams by constructing a punctuation graph; checking whether the punctuation graph is strongly connected and if so, indicating that the CJQ is safe to execute. The system uses a generalized punctuation graph and its transformation to support arbitrary punctuation schemes. The system also provides an efficient shared purge algorithm for multi-way join operator.

Abstract: Disclosed is a method and system for incrementally maintaining algebraic functions in automatic summary tables (ASTs) of at least one relational database. The method associates a work area with each algebraic function in each AST. Each work area is comprised of one or more variables. The method populates the variables within each work area for each algebraic function when each AST is created and when each AST is updated. The method maintains each work area by adding and subtracting to and from its associated variables when the associated data changes in the relational database. The functions that compute the variables of the work areas are distributive and thus incrementally maintainable. The method computes and recomputes the algebraic function within an AST after the updates have changed one or more of the variables of its workarea.