Lexis
Autor: | Constantine Dovrolis, Payam Siyari, Bistra Dilkina |
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Rok vydání: | 2016 |
Předmět: |
FOS: Computer and information sciences
0301 basic medicine Smallest grammar problem Lexis Theoretical computer science Optimization problem Hierarchy (mathematics) Computer Science - Artificial Intelligence Concatenation 020206 networking & telecommunications 02 engineering and technology Directed acyclic graph Substring Set (abstract data type) 03 medical and health sciences Artificial Intelligence (cs.AI) 030104 developmental biology Computer Science - Data Structures and Algorithms 0202 electrical engineering electronic engineering information engineering Data Structures and Algorithms (cs.DS) |
Zdroj: | KDD |
DOI: | 10.1145/2939672.2939741 |
Popis: | Data represented as strings abounds in biology, linguistics, document mining, web search and many other fields. Such data often have a hierarchical structure, either because they were artificially designed and composed in a hierarchical manner or because there is an underlying evolutionary process that creates repeatedly more complex strings from simpler substrings. We propose a framework, referred to as "Lexis", that produces an optimized hierarchical representation of a given set of "target" strings. The resulting hierarchy, "Lexis-DAG", shows how to construct each target through the concatenation of intermediate substrings, minimizing the total number of such concatenations or DAG edges. The Lexis optimization problem is related to the smallest grammar problem. After we prove its NP-Hardness for two cost formulations, we propose an efficient greedy algorithm for the construction of Lexis-DAGs. We also consider the problem of identifying the set of intermediate nodes (substrings) that collectively form the "core" of a Lexis-DAG, which is important in the analysis of Lexis-DAGs. We show that the Lexis framework can be applied in diverse applications such as optimized synthesis of DNA fragments in genomic libraries, hierarchical structure discovery in protein sequences, dictionary-based text compression, and feature extraction from a set of documents. |
Databáze: | OpenAIRE |
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