With the recent advancements of AI/ML, many tasks that were once unapproachable have become not. One of these tasks is asking questions to computers in a natural language and getting accurate and reasonable answers. Indeed, doing this task today is enabled by large language models that are notable for their ability to achieve general-purpose language generation and other natural language processing tasks.1
Tag: apache solr
Clustering is one of the most common unsupervised Machine Learning tasks. Solr is shipped with a clustering module based on Carrot2 built-in algorithms. Carrot2 comes with 4 algorithms: Lingo, STC, kMeans and Lingo3D each one mapped to a clustering engine. The first three are open-source whereas the last one is commercial. When this approach is used, clustering takes place in memory. Other frameworks, such as Mahout, can be used to do the clustering “off-line.”
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Solr + Superset
Apache Superset is a business intelligence SQL inclined platform equipped with a wide array of BI features and visualizations that satisfies data exploration and visualization requirements. It is battle tested in large environments with hundreds of concurrent users in production environments.
Graph traversal features have been introduced in Solr 6 releases. These powerful features enables Solr users to run expressions that traverses graph structures in order to introduce or extract useful information. These graph traversal features are particularly useful when data is already indexed into Solr and light graph operations are required especially on top of text search. Before proceeding, a basic knowledge of Solr and graph structures is required.
Solr traversal implementation uses Breadth First Search (BFS) to perform graph traversal which is more suitable for solving search problems than its counterpart Depth First Search (DFS). It is also possible to combine graph traversal with other search or streaming operations.
In this post, we are going to explore basic graph visualization introduced in Banana v1.7. To visualize a graph in Banana, there must be at least a collection indexed into Solr with two fields: from and to that represent the adjacency matrix or, in other words, the edges of the graph. Alternatively, two collections can be used to visualize the graph: a main collection which is configured in the dashboard settings and an additional graph collection that stores the graph matrix. The main collection will be joined with the graph collection to retrieve node labels.
TL;DR if you have a raw dataset or a data indexed into Apache Solr, a meaningful analytics dashboard that gives insights and useful graphical and tabular information can be built in minutes.