4 research outputs found
String Indexing for Top- Close Consecutive Occurrences
The classic string indexing problem is to preprocess a string into a
compact data structure that supports efficient subsequent pattern matching
queries, that is, given a pattern string , report all occurrences of
within . In this paper, we study a basic and natural extension of string
indexing called the string indexing for top- close consecutive occurrences
problem (SITCCO). Here, a consecutive occurrence is a pair , ,
such that occurs at positions and in and there is no occurrence
of between and , and their distance is defined as . Given a
pattern and a parameter , the goal is to report the top- consecutive
occurrences of in of minimal distance. The challenge is to compactly
represent while supporting queries in time close to length of and .
We give two time-space trade-offs for the problem. Let be the length of
, the length of , and . Our first result achieves
space and optimal query time of , and our second result
achieves linear space and query time . Along the way, we
develop several techniques of independent interest, including a new translation
of the problem into a line segment intersection problem and a new recursive
clustering technique for trees.Comment: Fixed typos, minor change
Efficient Indexing for Structured and Unstructured Data
The collection of digital data is growing at an exponential rate. Data originates from wide range of data sources such as text feeds, biological sequencers, internet traffic over routers, through sensors and many other sources. To mine intelligent information from these sources, users have to query the data. Indexing techniques aim to reduce the query time by preprocessing the data. Diversity of data sources in real world makes it imperative to develop application specific indexing solutions based on the data to be queried. Data can be structured i.e., relational tables or unstructured i.e., free text. Moreover, increasingly many applications need to seamlessly analyze both kinds of data making data integration a central issue. Integrating text with structured data needs to account for missing values, errors in the data etc. Probabilistic models have been proposed recently for this purpose. These models are also useful for applications where uncertainty is inherent in data e.g. sensor networks. This dissertation aims to propose efficient indexing solutions for several problems that lie at the intersection of database and information retrieval such as joining ranked inputs, full-text documents searching etc. Other well-known problems of ranked retrieval and pattern matching are also studied under probabilistic settings. For each problem, the worst-case theoretical bounds of the proposed solutions are established and/or their practicality is demonstrated by thorough experimentation