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In contrast, traditional graph techniques are based on a simple count of the frequency of a given relationship.This approach has the drawback that elements with the most connections -- the Shawshank Redemption in movie recommendation data or Starbucks in credit card purchase data -- are returned as the most important.With Graph, the Elastic Stack uses these statistics in new ways -- first to identify relationships within and across sets of documents, and then to prioritize the most relevant relationships for the given query." data-reactid="15"Graph Brings Relevance to Relationship Exploration When data is added to Elasticsearch, the indexing process tracks and counts the values in each field of the document, updates global frequencies, and prepares the data for a wide range of queries.
Graph automatically identifies the most important connections, separating the signal from the noise by employing relevance ranking specific to each query.
Because it is built on Elasticsearch, Graph benefits from high scalability and near-real-time data availability, enabling answers that evolve as your data changes.
MOUNTAIN VIEW, CA and AMSTERDAM, THE NETHERLANDS--(Marketwired - Mar 30, 2016) - Elastic today unveiled Graph, a new extension for Elasticsearch and Kibana that allows anyone to uncover, understand, and explore the relationships that live in their data.
By combining the speed and relevance-ranking of search with graph exploration, Graph opens up a whole host of new use-cases with the Elastic Stack."We built Graph to help you ask new types of questions about the data you store in Elasticsearch," said Steve Kearns, Sr. "By looking at the relationships in your data through the lens of relevance, it becomes easy to answer questions that previously would involve multiple systems, batch jobs or machine learning."Graph Enables New Use Cases for the Elastic Stack When you store data in Elasticsearch -- products, users, documents, logs -- this data often contains references or properties that represent connections between objects, entities, people, or machines.
The best way to explore these connections is to see them, which Graph provides via a Kibana plugin.
Like everything at Elastic, this UI is built on a simple, but powerful API that leverages Elastic's deep experience in relevance ranking to surface the most meaningful connections that live in your data.Follow up emails can help you build your email outreach after the purchase as well as before.It helps you learn whether your customers are happy with their product, encourages future purchases and attracts more customers.Our Q&A solution is fully customizable, providing a ready to use question and answer system for your website.Our Q&A widgets let customers ask questions about your products, while staying on your site within the purchase path.Push notifications are one of the most valuable capabilities of native apps, and this capability is now available on the web.We can push notification to your desktop when you receive new Customer Review or Question.This unique approach to graph exploration opens a wide range of new use-cases for the Elastic Stack, without requiring new index formats, by allowing users to query their existing data in new ways.Graph makes it easy to answer complex questions and address use-cases such as behavioral analysis, fraud, cybersecurity, drug discovery, personalized medicine, and to build personalized recommendations based on continuous real-time data.Since the launch of Rating System in 2008, we've watched the service grow rapidly with newly added features on a regular basis making Rating System the best it can be.We are now watching over 6000 companies / websites who have integrated our services generating in excess of 10 Million Page-Views per day*.