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Box is an add-on framework for Java EE applications that performs asynchronous gathering of source data with subsequent processing or transformation of that data into output JSON documents and exposing those documents through a web API. Box excels at maintaining existing documents including keeping them up-to-date in real time and deleting them when required. Box supports aggregating data from multiple sources as well as splitting data into multiple documents. How documents are processed is left up to each application. The web api makes the documents available by ID or by harvesting all documents. Harvests can be filtered using facets and documents can be pared down by requesting only needed fields.

TagSoup is a SAX-compliant parser written in Java that, instead of parsing well-formed or valid XML, parses HTML as it is found in the wild: poor, nasty and brutish, though quite often far from short. TagSoup is designed for people who have to process this stuff using some semblance of a rational application design. By providing a SAX interface, it allows standard XML tools to be applied to even the worst HTML. TagSoup also includes a command-line processor that reads HTML files and can generate either clean HTML or well-formed XML that is a close approximation to XHTML.

This module provides algorithms to compare sets, that is, order-invariant lists. These algorithms are implementations of the AlignmentAlgorithm interface defined in the algorithms module. In particular, this module contains the StrictSetAlignmentScoreAlgorithm for computing the cost of the optimal unordered alignment of two sets, the StrictSetAlignmentFullAlgorithm which provides the Alignment itself as well, and the GreedySetAlignmentScoreAlgorithm as well as the GreedySetAlignmentFullAlgorithm for computing a potentially sub-optimal but faster alignment of two sets. The optimal alignments rely on the HungarianAlgorithm for solving the assignment problem in bipartite graphs. Here, we rely on the implementation provided by Kevin L. Stern which is provided within this distribution.

This module contains the sequence datastructure of the TCS Alignment Toolbox. It defines the possible value sets in the ValueType enum as well as the different KeywordSpecification classes, namely: 1.) StringKeywordSpecification for string type values. 2.) SymbolicKeywordSpecification for values from a discrete alphabet (also refer to the Alphabet class) 3.) VectorialKeywordSpecification for vectors of some length (or for scalars) A NodeSpecification is a vector of such KeywordSpecifications and defines the order of value sets. A node, then, is defined as a vector of values from these value sets (also refer to the Value interface as well as the StringValue, SymbolicValue and VectorialValue classes). Finally a sequence is defined as a list of such nodes.

This module is a custom implementation of the Large Margin Nearest Neighbor classification scheme of Weinberger, Saul, et al. (2009). It contains an implementation of the k-nearest neighbor and LMNN classifier as well as (most importantly) gradient calculation schemes on the LMNN cost function given a sequential data set and a user-choice of alignment algorithm. This enables users to learn parameters of the alignment distance in question using a gradient descent on the LMNN cost function. More information on this approach can be found in the Masters Thesis "Adaptive Affine Sequence Alignment Using Algebraic Dynamic Programming"