SolrCommerce Integrations

DbVisualizer is one of the world’s most popular database clients. Developers, analysts, and DBAs use it to advance their SQL experience with modern tools to visualize and manage their databases, schemas, objects, and table data and to auto-generate, write and optimize queries. It has extended support for 30+ of the major databases and has basic-level support for all databases that can be accessed with a JDBC driver. DbVisualizer runs on all major OSes. Free and Pro versions are available.

Telegraf is an open-source server agent that helps you collect metrics from your sensors, stacks, and systems. Telegraf is a plugin-driven agent that collects and sends metrics and events from systems, databases, and IoT sensors. Telegraf is written in Go. It compiles to a single binary and has no external dependencies. It also requires very little memory. Telegraf can gather metrics from a wide variety of inputs and then write them into a wide range of outputs. It can be easily extended by being plugin-driven for both the collection and output data. It is written in Go and can be run on any system without external dependencies. It is easy to collect metrics from your endpoints with the 300+ plugins that have been created by data experts in the community.

*GraphDB allows the creation of large knowledge graphs by linking diverse data and indexing it for semantic search. * GraphDB is a robust and efficient graph database that supports RDF and SPARQL. The GraphDB database supports a highly accessible replication cluster. This has been demonstrated in a variety of enterprise use cases that required resilience for data loading and query answering. Visit the GraphDB product page for a quick overview and a link to download the latest releases. GraphDB uses RDF4J to store and query data. It also supports a wide range of query languages (e.g. SPARQL and SeRQL), and RDF syntaxes such as RDF/XML and Turtle.

NetOwl EntityMatcher offers a reliable, swift, and expandable solution for identity resolution, taking into account not just the similarities in entity names but also critical attributes like date of birth, place of birth, address, and nationality. Additionally, it allows for identity resolution using social network details, including information about an individual's employer, spouse, or associates. Utilizing its proprietary search and indexing engine, NetOwl can combine evidence from various entity record attributes, providing a highly effective, scalable, and user-friendly approach to matching. Users can define application-specific business rules that dictate which combinations of record attributes need to be matched and establish the importance of each attribute. Furthermore, by incorporating our machine learning-driven multicultural and multilingual name matching solution, NetOwl NameMatcher, the system enhances the sophistication of name matching across different types of entities. This integration ensures that identity resolution maintains a high level of accuracy and adaptability in diverse scenarios.

NetOwl Extractor provides exceptionally precise, rapid, and scalable entity extraction across various languages through the use of AI-driven natural language processing and machine learning techniques. This named entity recognition tool can be utilized both on-site and in the cloud, facilitating a wide range of Big Data Text Analytics applications. Supporting over 100 distinct entity types, NetOwl presents a comprehensive semantic ontology for entity extraction that surpasses conventional named entity extraction tools. Its offerings encompass individuals, numerous organization categories (such as corporations and government entities), diverse geographic locations (including nations and cities), as well as addresses, artifacts, phone numbers, and titles. This extensive named entity recognition (NER) serves as a crucial basis for more sophisticated relationship and event extraction processes. The software is applicable across various sectors, including Business, Finance, Politics, Homeland Security, Law Enforcement, Military, National Security, and Social Media, making it a versatile choice for organizations seeking in-depth textual analysis. Furthermore, its adaptability to different environments ensures that users can effectively harness its capabilities to meet their specific needs.

NetOwl TextMiner merges the acclaimed NetOwl Extractor with Elasticsearch to deliver an innovative text analytics solution. This software harnesses the full spectrum of NetOwl's functionalities, making it perfect for conducting "what if" analyses, performing discovery tasks, facilitating quick-response investigations, and carrying out thorough research. By incorporating all the text analytics features of the NetOwl Extractor—including entity extraction, relationship and event extraction, sentiment analysis, text categorization, and geotagging—TextMiner presents a comprehensive text mining platform. The results generated by the Extractor are stored within Elasticsearch, which offers a range of intelligent search and analytical capabilities. The synergy between Elasticsearch and NetOwl ensures rapid and scalable real-time text analysis suited for handling Big Data. Furthermore, the user-friendly web-based interface of TextMiner can be easily configured to accommodate various analytical needs, enabling users to swiftly access only the most valuable insights from extensive text datasets. This adaptability not only enhances usability but also allows for more tailored analysis across multiple domains.

NetOwl NameMatcher, recognized for its excellence in the MITRE Multicultural Name Matching Challenge, delivers unparalleled accuracy, speed, and scalability in name matching solutions. By employing an innovative machine learning framework, NetOwl effectively tackles the intricate challenges of fuzzy name matching. Conventional methods like Soundex, edit distance, and rule-based systems often face significant issues with precision, leading to false positives, and recall, resulting in false negatives, when confronting the diverse fuzzy name matching scenarios outlined previously. In contrast, NetOwl leverages a data-driven, machine learning-based probabilistic strategy to address these name matching difficulties. It automatically generates sophisticated, probabilistic name matching rules from extensive, real-world multi-ethnic name variant datasets. Furthermore, NetOwl employs distinct matching models tailored to various entity types, such as individuals, organizations, and locations. To add to its capabilities, NetOwl also integrates automatic detection of name ethnicity, enhancing its adaptability to the complexities of multicultural name matching. This comprehensive approach ensures a higher level of accuracy and reliability in diverse applications.

Underneath a well-established front-end, Broadleaf's Headless Commerce Solution empowers you to create a top-tier commerce platform. Leverage our API layer to consolidate commerce management and backend operations while facilitating various applications including web, point of sale, and mobile. Broadleaf's APIs come as microservices encompassing over 30 independently deployable and scalable bounded contexts. These services collaborate effectively to satisfy the demands of any modern commerce solution. However, merely being headless isn't sufficient. With Broadleaf, you will have access to more than 1,000 API endpoints, each supported by fully customizable Java and Spring components. This robust functionality is presented through an admin application that employs metadata to offer backend users a comprehensive and engaging experience, all while allowing for independently maintainable microservices. Furthermore, Broadleaf enables you to oversee the experience for multiple brands, each with its own distinct carts, catalogs, customers, and orders, ensuring a tailored approach to commerce management. With these capabilities, your commerce platform can adapt seamlessly to evolving market needs.