Showing papers on "Interval tree published in 2021"

Efficient Trajectory Contact Query Processing

Abstract: During an infectious disease outbreak, the contact tracing is regarded as the most crucial and effective way of disease control. As the users’ trajectories are widely obtainable due to the ubiquity of positioning devices, the contact tracing can be achieved by examining trajectories of confirmed patients to identify other trajectories that are contacted either directly or indirectly. In this paper, we propose a generalised Trajectory Contact Search (TCS) query, which models the contact tracing problem as well as other similar trajectory-based problems. In addition, we answer the query by proposing an iterative algorithm that finds contacted trajectories progressively along the transmission chains, and we further optimise each iteration in terms of time and space efficiency by proposing a hop scanning algorithm and a grid-based time interval tree. Extensive experiments on large-scale real-world data demonstrate the effectiveness of our proposed solutions over baseline algorithms.

Bedtk: finding interval overlap with implicit interval tree.

Abstract: Summary We present bedtk, a new toolkit for manipulating genomic intervals in the BED format. It supports sorting, merging, intersection, subtraction and the calculation of the breadth of coverage. Bedtk employs implicit interval tree, a data structure for fast interval overlap queries. It is several to tens of times faster than existing tools and tends to use less memory. Availability https://github.com/lh3/bedtk. Supplementary information Supplementary data are available at Bioinformatics online.

Query co-planning for shared execution in Key-Value Stores

Abstract: Key-value stores propose a straightforward yet powerful data model. Data is modeled using key-value pairs where values can be arbitrary objects and written/read using the key associated with it. In addition to their simple interface, such data stores also provide read operations such as full and range scans. However, due to the simplicity of its interface, trying to optimize data accesses becomes challenging. This work aims to enable the shared execution of concurrent range and point queries on key-value stores. Thus, reducing the overall data movement when executing a complete workload. To accomplish this, we analyze different possible data structures and propose our variation of a segment tree, Updatable Interval Tree. Our data structure helps us co-planning and co-executing multiple range queries together and reduces redundant work. This results in executing workloads more efficiently and overall increased throughput, as we show in our evaluation.

Fast incremental PEG parsing

Abstract: Incremental parsing is an integral part of code analysis performed by text editors and integrated development environments. This paper presents new methods to significantly improve the efficiency of incremental parsing for Parsing Expression Grammars (PEGs). We build on Incremental Packrat Parsing, an algorithm that adapts packrat parsing to an incremental setting, by implementing the memoization table as an interval tree with special support for shifting intervals, and modifying the memoization strategy to create tree structures in the table. Our approach enables reparsing in time logarithmic in the size of the input for typical edits, compared with linear-time reparsing for Incremental Packrat Parsing. We implement our methods in a prototype called GPeg, a parsing machine for PEGs with support for dynamic parsers (an important feature for extensibility in editors). Experiments show that GPeg has strong performance (sub-5ms reparse times) across a variety of input sizes (tens to hundreds of megabytes) and grammar types (from full language grammars to minimal grammars), and compares well with existing incremental parsers. As a complete example, we implement a syntax highlighting library and prototype editor using GPeg, with optimizations for these applications.