Skip list

About: Skip list is a research topic. Over the lifetime, 375 publications have been published within this topic receiving 10382 citations. The topic is also known as: jump list.

Skip lists: a probabilistic alternative to balanced trees

Abstract: Skip lists are data structures that use probabilistic balancing rather than strictly enforced balancing. As a result, the algorithms for insertion and deletion in skip lists are much simpler and significantly faster than equivalent algorithms for balanced trees.

Skip Lists: A Probabilistic Alternative to Balanced Trees

Abstract: Skip lists are a practical, probabilistic data structure that can be used in place of balanced trees. Algorithms for insertion and deletion in skip lists are much simpler and significantly faster than equivalent algorithms for balanced trees. This paper describes and analyzes skip lists and presents new techniques for analyzing probabilistic algorithms.

Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs

Abstract: We present a new approach for the approximate K-nearest neighbor search based on navigable small world graphs with controllable hierarchy (Hierarchical NSW, HNSW). The proposed solution is fully graph-based, without any need for additional search structures (typically used at the coarse search stage of the most proximity graph techniques). Hierarchical NSW incrementally builds a multi-layer structure consisting of a hierarchical set of proximity graphs (layers) for nested subsets of the stored elements. The maximum layer in which an element is present is selected randomly with an exponentially decaying probability distribution. This allows producing graphs similar to the previously studied Navigable Small World (NSW) structures while additionally having the links separated by their characteristic distance scales. Starting the search from the upper layer together with utilizing the scale separation boosts the performance compared to NSW and allows a logarithmic complexity scaling. Additional employment of a heuristic for selecting proximity graph neighbors significantly increases performance at high recall and in case of highly clustered data. Performance evaluation has demonstrated that the proposed general metric space search index is able to strongly outperform previous opensource state-of-the-art vector-only approaches. Similarity of the algorithm to the skip list structure allows straightforward balanced distributed implementation.

Skip graphs

Abstract: Skip graphs are a novel distributed data structure, based on skip lists, that provide the full functionality of a balanced tree in a distributed system where elements are stored in separate nodes that may fail at any time. They are designed for use in searching peer-to-peer networks, and by providing the ability to perform queries based on key ordering, they improve on existing search tools that provide only hash table functionality. Unlike skip lists or other tree data structures, skip graphs are highly resilient, tolerating a large fraction of failed nodes without losing connectivity. In addition, constructing, inserting new elements into, searching a skip graph and detecting and repairing errors in the data structure introduced by node failures can be done using simple and straight-forward algorithms.

Data Structures and Algorithm Analysis in C

Abstract: From the Publisher: Mark Allen Weiss provides a proven approach to algorithms and data structures using the exciting Java programming language as the implementation tool. With Java he highlights conceptual topics, focusing on ADTs and the analysis of algorithms for efficiency as well as performance and running time. Dr. Weiss also distinguishes this text with a logical organization of topics, his engaging writing style, and an extensive use of figures and examples showing the successive stages of an algorithm. Features: Contains extensive sample code using Java 1.2, which is available over the Internet. Covers the Java Collections Library in an appendix. Includes a chapter on algorithm and design techniques that covers greedy algorithms, divide-and-conquer algorithms, dynamic programming, randomized algorithms, and backtracking. Presents current topics and new data structures such as Fibonacci heaps, skew heaps, binomial queues, skip lists, and splay trees. Offers a chapter on amortized analysis that examines the advanced data structures presented earlier in the book. Provides a chapter on advanced data structures and their implementation, covering red-black trees, top-down splay trees, treaps, k-d trees, pairing heaps, and more.