scholarly journals A distributed algorithm to maintain and repair the trail networks of arboreal ants

2017 ◽  
Author(s):  
Arjun Chandrasekhar ◽  
Deborah M. Gordon ◽  
Saket Navlakha
Keyword(s):  
Real World ◽  
Distributed Algorithm ◽  
Field Data ◽  
Search Algorithms ◽  
Food Sources ◽  
Graph Search ◽  
Probabilistic Algorithm ◽  
Graph Search Algorithms ◽  
Computational Resources ◽  
Molecular Robots

AbstractWe study how the arboreal turtle ant (Cephalotes goniodontus) solves a fundamental computing problem: maintaining a trail network and finding alternative paths to route around broken links in the network. Turtle ants form a routing backbone of foraging trails linking several nests and temporary food sources. This species travels only in the trees, so their foraging trails are constrained to lie on a natural graph formed by overlapping branches and vines in the tangled canopy. Links between branches, however, can be ephemeral, easily destroyed by wind, rain, or animal movements. Here we report a biologically feasible distributed algorithm, parameterized using field data, that can plausibly describe how turtle ants maintain the routing backbone and find alternative paths to circumvent broken links in the backbone. We validate the ability of this probabilistic algorithm to circumvent simulated breaks in synthetic and real-world networks, and we derive an analytic explanation for why certain features are crucial to improve the algorithm’s success. Our proposed algorithm uses fewer computational resources than common distributed graph search algorithms, and thus may be useful in other domains, such as for swarm computing or for coordinating molecular robots.

Formalization of graph search algorithms and its applications

1998 ◽  
pp. 479-496 ◽  
Author(s):  
Mitsuharu Yamamoto ◽  
Koichi Takahashi ◽  
Masami Hagiya ◽  
Shin-ya Nishizaki ◽  
Tetsuo Tamai
Keyword(s):  
Search Algorithms ◽  
Graph Search ◽  
Graph Search Algorithms

scholarly journals Search and Graph Database Technologies for Biomedical Semantic Indexing: Experimental Analysis (Preprint)

2017 ◽  
Author(s):  
Isabel Segura Bedmar ◽  
Paloma Martínez ◽  
Adrián Carruana Martín
Keyword(s):  
Search Algorithms ◽  
Biomedical Literature ◽  
Graph Search ◽  
Semantic Indexing ◽  
Graph Database ◽  
Medical Subject Headings ◽  
Support Tool ◽  
Mesh Terms ◽  
Lowest Common Ancestor ◽  
Graph Search Algorithms

BACKGROUND Biomedical semantic indexing is a very useful support tool for human curators in their efforts for indexing and cataloging the biomedical literature. OBJECTIVE The aim of this study was to describe a system to automatically assign Medical Subject Headings (MeSH) to biomedical articles from MEDLINE. METHODS Our approach relies on the assumption that similar documents should be classified by similar MeSH terms. Although previous work has already exploited the document similarity by using a k-nearest neighbors algorithm, we represent documents as document vectors by search engine indexing and then compute the similarity between documents using cosine similarity. Once the most similar documents for a given input document are retrieved, we rank their MeSH terms to choose the most suitable set for the input document. To do this, we define a scoring function that takes into account the frequency of the term into the set of retrieved documents and the similarity between the input document and each retrieved document. In addition, we implement guidelines proposed by human curators to annotate MEDLINE articles; in particular, the heuristic that says if 3 MeSH terms are proposed to classify an article and they share the same ancestor, they should be replaced by this ancestor. The representation of the MeSH thesaurus as a graph database allows us to employ graph search algorithms to quickly and easily capture hierarchical relationships such as the lowest common ancestor between terms. RESULTS Our experiments show promising results with an F1 of 69% on the test dataset. CONCLUSIONS To the best of our knowledge, this is the first work that combines search and graph database technologies for the task of biomedical semantic indexing. Due to its horizontal scalability, ElasticSearch becomes a real solution to index large collections of documents (such as the bibliographic database MEDLINE). Moreover, the use of graph search algorithms for accessing MeSH information could provide a support tool for cataloging MEDLINE abstracts in real time.

scholarly journals Facilitating Large-Scale Graph Search Algorithms with Lock-Free Concurrent Pairing Heaps

2019 ◽  
Author(s):  
Jeremy Mayeres ◽  
Charles Newton ◽  
Helena Arpudaraj
Keyword(s):  
Data Structures ◽  
Shortest Path ◽  
Large Scale ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Graph Search ◽  
Dijkstra’S Algorithm ◽  
Single Source ◽  
Dijkstra's Algorithm ◽  
Graph Search Algorithms

This paper introduces a lock-free version of a Pairing heap. Dijkstra's algorithm is a search algorithm to solve the single-source shortest path problem. The performance of Dijkstra's algorithm improves when threads can also perform work concurrently (in particular, when decreaseKey calls occur concurrently.) However, current implementations of decreaseKey on popular backing data structures such as Pairing heaps and Fibonacci heaps severely limit concurrency. Lock-free techniques can improve the concurrency of search structures such as heaps. In this paper we introduce decreaseKey and insert operators for Pairing heaps that provide lock-free guarantees while still running in constant time.

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