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Lazo - High-Dimensional Data Search

Lazo is a library for approximate high-dimensional search. It can index large volumes of high-dimensional points, search among them efficiently, and give an approximate answer with high accuracy. Right now Lazo supports two different search metrics, Jaccard similarity and Jaccard containment; more metrics are under development and will be made available soon.

We are using Lazo within the context of data discovery, and in particular, Aurum. For more information take a look at this project and the relevant papers in my webpage.

When should I use Lazo?

This is a non-exhaustive list of situations where you may benefit from Lazo:

  • When you are trying to solve an approximate nearest neighbor problem and your metric of interest is Jaccard similarity (Lazo implements MinHash/LSH) or Jaccard containment.

  • When you want to find all-pairs of elements within a set N with a Jaccard similarity or containment beyond a given threshold, and you cannot afford actually comparing every pair, but you are ok with approximate answers.

Quick Start

Building Project

Lazo will be made available through a Maven repository in the future. For the time being, it must be built locally, which is simple:

From the root directory of the project execute the following command:

$> ./gradlew build

this builds the library and runs the tests. The output will be in a repository build in the root directory of the project. For convenience, this command creates a jar file, which you can easily link to your project.

Note: Lazo uses the Gradle build tool. The project includes the gradle wrapper, which means you don't need to install anything: the wrapper will download all the necessary files of the build system.

Basic use of the library

With Lazo you can index efficiently very high numbers of sets (N) and then quickly search for those that are similar to a query set (q). The workflow for that is

  1. to create a sketch (a succint summary of the data) of each set you want to search; 2) insert the sketch into the Lazo index; and 3) query the index with the sketch for q.

Creating a sketch of a set: Creating a sketch of a set of values:

LazoSketch sketch = new LazoSketch();
for (String value : values) {
    sketch.update(value);
}

the method update takes a string as input parameter. You can update the sketch as data becomes available if that's necessary.

Indexing a sketch: To index the sketch in the Lazo Index is as simple as:

LazoIndex index = new LazoIndex();
index.insert(<key>, sketch);

Note the key argument of the insert function can be any Java Object.

Querying the index: To query the index, i.e., find all sketches similar to an input sketch q:

Set<LazoCandidate> results = index.querySimilarity(q, <similarity_threshold>);
Set<LazoCandidate> results = index.queryContainment(q, <containment_threshold>);

The similarity_ and containment_threshold must be a floating number in the range [0,1]. The object LazoCandidate contains the used to identify the sketch when it was inserted as well as the specific Jaccard similarity and containment with respect to the input query sketch, q.

Support or Contact

Docs are being built, if you are interested in contributing to this project, you can reach me at raulcf@csail.mit.edu

About

Sketch and LSH Index library for Java, including OPH methods as well as the Lazo method

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