Pub Date : 2020-01-01DOI: 10.4230/LIPIcs.SEA.2020.16
J. Sauer, D. Wagner, T. Zündorf
We study multi-modal route planning in a network comprised of schedule-based public transportation, unrestricted walking, and cycling with bikes available from bike sharing stations. So far this problem has only been considered for scenarios with at most one bike sharing operator, for which MCR is the best known algorithm [Delling et al., 2013]. However, for practical applications, algorithms should be able to distinguish between bike sharing stations of multiple competing bike sharing operators. Furthermore, MCR has recently been outperformed by ULTRA for multi-modal route planning scenarios without bike sharing [Baum et al., 2019]. In this paper, we present two approaches for modeling multi-modal transportation networks with multiple bike sharing operators: The operator-dependent model requires explicit handling of bike sharing stations within the algorithm, which we demonstrate with an adapted version of MCR. In the operator-expanded model, all relevant information is encoded within an expanded network. This allows for applying any multi-modal public transit algorithm without modification, which we show for ULTRA. We proceed by describing an additional preprocessing step called operator pruning, which can be used to accelerate both approaches. We conclude our work with an extensive experimental evaluation on the networks of London, Switzerland, and Germany. Our experiments show that the new preprocessing technique accelerates both approaches significantly, with the fastest algorithm (ULTRA-RAPTOR with operator pruning) being more than an order of magnitude faster than the basic MCR approach. Moreover, the ULTRA preprocessing step also benefits from operator pruning, as its running time is reduced by a factor of 14 to 20.
我们研究了一个由基于时间表的公共交通、不受限制的步行和自行车共享站提供的自行车组成的网络中的多模式路线规划。到目前为止，这个问题只考虑了最多有一个共享单车运营商的场景，其中MCR是最著名的算法[Delling et al.， 2013]。然而，在实际应用中，算法应该能够区分多个竞争的共享单车运营商的共享单车站点。此外，在没有共享单车的多模式路线规划场景中，MCR最近被ULTRA超越[Baum等人，2019]。在本文中，我们提出了两种具有多个共享单车运营商的多模式交通网络建模方法:运营商依赖模型需要在算法中明确处理共享单车站点，我们用MCR的改编版本进行了演示。在运营商扩展模型中，所有相关信息都在扩展的网络中编码。这允许在不修改的情况下应用任何多模式公共交通算法，我们为ULTRA展示了这一点。接下来，我们将描述一个额外的预处理步骤，称为算子剪枝，它可以用来加速这两种方法。最后，我们对伦敦、瑞士和德国的网络进行了广泛的实验评估。我们的实验表明，新的预处理技术显著加快了这两种方法的速度，最快的算法(带有算子修剪的ULTRA-RAPTOR)比基本的MCR方法快一个数量级以上。此外，ULTRA预处理步骤也受益于操作员修剪，因为它的运行时间减少了14到20倍。
{"title":"Faster Multi-Modal Route Planning With Bike Sharing Using ULTRA","authors":"J. Sauer, D. Wagner, T. Zündorf","doi":"10.4230/LIPIcs.SEA.2020.16","DOIUrl":"https://doi.org/10.4230/LIPIcs.SEA.2020.16","url":null,"abstract":"We study multi-modal route planning in a network comprised of schedule-based public transportation, unrestricted walking, and cycling with bikes available from bike sharing stations. So far this problem has only been considered for scenarios with at most one bike sharing operator, for which MCR is the best known algorithm [Delling et al., 2013]. However, for practical applications, algorithms should be able to distinguish between bike sharing stations of multiple competing bike sharing operators. Furthermore, MCR has recently been outperformed by ULTRA for multi-modal route planning scenarios without bike sharing [Baum et al., 2019]. In this paper, we present two approaches for modeling multi-modal transportation networks with multiple bike sharing operators: The operator-dependent model requires explicit handling of bike sharing stations within the algorithm, which we demonstrate with an adapted version of MCR. In the operator-expanded model, all relevant information is encoded within an expanded network. This allows for applying any multi-modal public transit algorithm without modification, which we show for ULTRA. We proceed by describing an additional preprocessing step called operator pruning, which can be used to accelerate both approaches. We conclude our work with an extensive experimental evaluation on the networks of London, Switzerland, and Germany. Our experiments show that the new preprocessing technique accelerates both approaches significantly, with the fastest algorithm (ULTRA-RAPTOR with operator pruning) being more than an order of magnitude faster than the basic MCR approach. Moreover, the ULTRA preprocessing step also benefits from operator pruning, as its running time is reduced by a factor of 14 to 20.","PeriodicalId":9448,"journal":{"name":"Bulletin of the Society of Sea Water Science, Japan","volume":"1 1","pages":"16:1-16:14"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77017368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.4230/LIPIcs.SEA.2020.9
Yu Nakahata, Masaaki Nishino, J. Kawahara, S. Minato
Subgraph enumeration is a fundamental task in computer science. Since the number of subgraphs can be large, some enumeration algorithms exploit compressed representations for efficiency. One such representation is the Zero-suppressed Binary Decision Diagram (ZDD). ZDDs can represent the set of subgraphs compactly and support several poly-time queries, such as counting and random sampling. Researchers have proposed efficient algorithms to construct ZDDs representing the set of subgraphs under several constraints, which yield fruitful results in many applications. Recently, Zero-suppressed Sentential Decision Diagrams (ZSDDs) have been proposed as variants of ZDDs. ZSDDs can be smaller than ZDDs when representing the same set of subgraphs. However, efficient algorithms to construct ZSDDs are known only for specific types of subgraphs: matchings and paths. We propose a novel framework to construct ZSDDs representing sets of subgraphs under given constraints. Using our framework, we can construct ZSDDs representing several sets of subgraphs such as matchings, paths, cycles, and spanning trees. We show the bound of sizes of constructed ZSDDs by the branch-width of the input graph, which is smaller than that of ZDDs by the pathwidth. Experiments show that our methods can construct ZSDDs faster than ZDDs and that the constructed ZSDDs are smaller than ZDDs when representing the same set of subgraphs. 2012 ACM Subject Classification Mathematics of computing → Graph algorithms
{"title":"Enumerating All Subgraphs Under Given Constraints Using Zero-Suppressed Sentential Decision Diagrams","authors":"Yu Nakahata, Masaaki Nishino, J. Kawahara, S. Minato","doi":"10.4230/LIPIcs.SEA.2020.9","DOIUrl":"https://doi.org/10.4230/LIPIcs.SEA.2020.9","url":null,"abstract":"Subgraph enumeration is a fundamental task in computer science. Since the number of subgraphs can be large, some enumeration algorithms exploit compressed representations for efficiency. One such representation is the Zero-suppressed Binary Decision Diagram (ZDD). ZDDs can represent the set of subgraphs compactly and support several poly-time queries, such as counting and random sampling. Researchers have proposed efficient algorithms to construct ZDDs representing the set of subgraphs under several constraints, which yield fruitful results in many applications. Recently, Zero-suppressed Sentential Decision Diagrams (ZSDDs) have been proposed as variants of ZDDs. ZSDDs can be smaller than ZDDs when representing the same set of subgraphs. However, efficient algorithms to construct ZSDDs are known only for specific types of subgraphs: matchings and paths. We propose a novel framework to construct ZSDDs representing sets of subgraphs under given constraints. Using our framework, we can construct ZSDDs representing several sets of subgraphs such as matchings, paths, cycles, and spanning trees. We show the bound of sizes of constructed ZSDDs by the branch-width of the input graph, which is smaller than that of ZDDs by the pathwidth. Experiments show that our methods can construct ZSDDs faster than ZDDs and that the constructed ZSDDs are smaller than ZDDs when representing the same set of subgraphs. 2012 ACM Subject Classification Mathematics of computing → Graph algorithms","PeriodicalId":9448,"journal":{"name":"Bulletin of the Society of Sea Water Science, Japan","volume":"224 1","pages":"9:1-9:14"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89176130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-19DOI: 10.4230/LIPIcs.SEA.2020.15
Tobias Maier, P. Sanders, Robert Williger
A quotient filter is a cache efficient AMQ data structure. Depending on the fill degree of the filter most insertions and queries only need to access one or two consecutive cache lines. This makes quotient filters fast compared to the more commonly used Bloom filters that incur multiple cache misses. However, concurrent Bloom filters are easy to implement and can be implemented lock-free while concurrent quotient filters are not as simple. Usually concurrent quotient filters work by using an external array of locks -- each protecting a region of the table. Accessing this array incurs one additional cache miss per operation. We propose a new locking scheme that has no memory overhead. Using this new locking scheme we achieve 1.8 times higher speedups than with the common external locking scheme. Another advantage of quotient filters over Bloom filters is that a quotient filter can change its size when it is becoming full. We implement this growing technique for our concurrent quotient filters and adapt it in a way that allows unbounded growing while keeping a bounded false positive rate. We call the resulting data structure a fully expandable quotient filter. Its design is similar to scalable Bloom filters, but we exploit some concepts inherent to quotient filters to improve the space efficiency and the query speed. We also propose quotient filter variants that are aimed to reduce the number of status bits (2-status-bit variant) or to simplify concurrent implementations (linear probing quotient filter). The linear probing quotient filter even leads to a lock-free concurrent filter implementation. This is especially interesting, since we show that any lock-free implementation of another common quotient filter variant would incur significant overheads in the form of additional data fields or multiple passes over the accessed data.
{"title":"Concurrent Expandable AMQs on the Basis of Quotient Filters","authors":"Tobias Maier, P. Sanders, Robert Williger","doi":"10.4230/LIPIcs.SEA.2020.15","DOIUrl":"https://doi.org/10.4230/LIPIcs.SEA.2020.15","url":null,"abstract":"A quotient filter is a cache efficient AMQ data structure. Depending on the fill degree of the filter most insertions and queries only need to access one or two consecutive cache lines. This makes quotient filters fast compared to the more commonly used Bloom filters that incur multiple cache misses. However, concurrent Bloom filters are easy to implement and can be implemented lock-free while concurrent quotient filters are not as simple. Usually concurrent quotient filters work by using an external array of locks -- each protecting a region of the table. Accessing this array incurs one additional cache miss per operation. We propose a new locking scheme that has no memory overhead. Using this new locking scheme we achieve 1.8 times higher speedups than with the common external locking scheme. \u0000Another advantage of quotient filters over Bloom filters is that a quotient filter can change its size when it is becoming full. We implement this growing technique for our concurrent quotient filters and adapt it in a way that allows unbounded growing while keeping a bounded false positive rate. We call the resulting data structure a fully expandable quotient filter. Its design is similar to scalable Bloom filters, but we exploit some concepts inherent to quotient filters to improve the space efficiency and the query speed. \u0000We also propose quotient filter variants that are aimed to reduce the number of status bits (2-status-bit variant) or to simplify concurrent implementations (linear probing quotient filter). The linear probing quotient filter even leads to a lock-free concurrent filter implementation. This is especially interesting, since we show that any lock-free implementation of another common quotient filter variant would incur significant overheads in the form of additional data fields or multiple passes over the accessed data.","PeriodicalId":9448,"journal":{"name":"Bulletin of the Society of Sea Water Science, Japan","volume":"115 ","pages":"15:1-15:13"},"PeriodicalIF":0.0,"publicationDate":"2019-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91447660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.5040/9781350076747.ch-004
{"title":"Full of Gods","authors":"","doi":"10.5040/9781350076747.ch-004","DOIUrl":"https://doi.org/10.5040/9781350076747.ch-004","url":null,"abstract":"","PeriodicalId":9448,"journal":{"name":"Bulletin of the Society of Sea Water Science, Japan","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85988959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.5040/9781350076747.ch-003
{"title":"Forwards and Backwards—Catastrophe?","authors":"","doi":"10.5040/9781350076747.ch-003","DOIUrl":"https://doi.org/10.5040/9781350076747.ch-003","url":null,"abstract":"","PeriodicalId":9448,"journal":{"name":"Bulletin of the Society of Sea Water Science, Japan","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74741825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.5040/9781350076747.ch-002
{"title":"Amniotica","authors":"","doi":"10.5040/9781350076747.ch-002","DOIUrl":"https://doi.org/10.5040/9781350076747.ch-002","url":null,"abstract":"","PeriodicalId":9448,"journal":{"name":"Bulletin of the Society of Sea Water Science, Japan","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89807552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.5040/9781350076747.ch-001
{"title":"Let Ourselves Be Cradled","authors":"","doi":"10.5040/9781350076747.ch-001","DOIUrl":"https://doi.org/10.5040/9781350076747.ch-001","url":null,"abstract":"","PeriodicalId":9448,"journal":{"name":"Bulletin of the Society of Sea Water Science, Japan","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84361814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.5040/9781350076747.ch-007
{"title":"Waves and Drops of Time","authors":"","doi":"10.5040/9781350076747.ch-007","DOIUrl":"https://doi.org/10.5040/9781350076747.ch-007","url":null,"abstract":"","PeriodicalId":9448,"journal":{"name":"Bulletin of the Society of Sea Water Science, Japan","volume":"2001 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88302844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-27DOI: 10.4230/LIPIcs.SEA.2018.17
D. Belazzougui, F. Cunial, Olgert Denas
Computing the matching statistics of a string S with respect to a string T on an alphabet of size sigma is a fundamental primitive for a number of large-scale string analysis applications, including the comparison of entire genomes, for which space is a pressing issue. This paper takes from theory to practice an existing algorithm that uses just O(|T|log{sigma}) bits of space, and that computes a compact encoding of the matching statistics array in O(|S|log{sigma}) time. The techniques used to speed up the algorithm are of general interest, since they optimize queries on the existence of a Weiner link from a node of the suffix tree, and parent operations after unsuccessful Weiner links. Thus, they can be applied to other matching statistics algorithms, as well as to any suffix tree traversal that relies on such calls. Some of our optimizations yield a matching statistics implementation that is up to three times faster than a plain version of the algorithm, depending on the similarity between S and T. In genomic datasets of practical significance we achieve speedups of up to 1.8, but our fastest implementations take on average twice the time of an existing code based on the LCP array. The key advantage is that our implementations need between one half and one fifth of the competitor's memory, and they approach comparable running times when S and T are very similar.
{"title":"Fast matching statistics in small space","authors":"D. Belazzougui, F. Cunial, Olgert Denas","doi":"10.4230/LIPIcs.SEA.2018.17","DOIUrl":"https://doi.org/10.4230/LIPIcs.SEA.2018.17","url":null,"abstract":"Computing the matching statistics of a string S with respect to a string T on an alphabet of size sigma is a fundamental primitive for a number of large-scale string analysis applications, including the comparison of entire genomes, for which space is a pressing issue. This paper takes from theory to practice an existing algorithm that uses just O(|T|log{sigma}) bits of space, and that computes a compact encoding of the matching statistics array in O(|S|log{sigma}) time. The techniques used to speed up the algorithm are of general interest, since they optimize queries on the existence of a Weiner link from a node of the suffix tree, and parent operations after unsuccessful Weiner links. Thus, they can be applied to other matching statistics algorithms, as well as to any suffix tree traversal that relies on such calls. Some of our optimizations yield a matching statistics implementation that is up to three times faster than a plain version of the algorithm, depending on the similarity between S and T. In genomic datasets of practical significance we achieve speedups of up to 1.8, but our fastest implementations take on average twice the time of an existing code based on the LCP array. The key advantage is that our implementations need between one half and one fifth of the competitor's memory, and they approach comparable running times when S and T are very similar.","PeriodicalId":9448,"journal":{"name":"Bulletin of the Society of Sea Water Science, Japan","volume":"35 1","pages":"17:1-17:14"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74631490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-06-27DOI: 10.4230/LIPIcs.SEA.2018.18
Bastien Cazaux, S. Juhel, Eric Rivals
Given a set P of words, the Shortest Linear Superstring (SLS) problem is an optimisation problem that asks for a superstring of P of minimal length. SLS has applications in data compression, where a superstring is a compact representation of P, and in bioinformatics where it models the first step of genome assembly. Unfortunately SLS is hard to solve (NP-hard) and to closely approximate (MAX-SNP-hard). If numerous polynomial time approximation algorithms have been devised, few articles report on their practical performance. We lack knowledge about how closely an approximate superstring can be from an optimal one in practice. Here, we exhibit a linear time algorithm that reports an upper and a lower bound on the length of an optimal superstring. The upper bound is the length of an approximate superstring. This algorithm can be used to evaluate beforehand whether one can get an approximate superstring whose length is close to the optimum for a given instance. Experimental results suggest that its approximation performance is orders of magnitude better than previously reported practical values. Moreover, the proposed algorithm remainso efficient even on large instances and can serve to explore in practice the approximability of SLS.
{"title":"Practical lower and upper bounds for the Shortest Linear Superstring","authors":"Bastien Cazaux, S. Juhel, Eric Rivals","doi":"10.4230/LIPIcs.SEA.2018.18","DOIUrl":"https://doi.org/10.4230/LIPIcs.SEA.2018.18","url":null,"abstract":"Given a set P of words, the Shortest Linear Superstring (SLS) problem is an optimisation problem that asks for a superstring of P of minimal length. SLS has applications in data compression, where a superstring is a compact representation of P, and in bioinformatics where it models the first step of genome assembly. Unfortunately SLS is hard to solve (NP-hard) and to closely approximate (MAX-SNP-hard). If numerous polynomial time approximation algorithms have been devised, few articles report on their practical performance. We lack knowledge about how closely an approximate superstring can be from an optimal one in practice. Here, we exhibit a linear time algorithm that reports an upper and a lower bound on the length of an optimal superstring. The upper bound is the length of an approximate superstring. This algorithm can be used to evaluate beforehand whether one can get an approximate superstring whose length is close to the optimum for a given instance. Experimental results suggest that its approximation performance is orders of magnitude better than previously reported practical values. Moreover, the proposed algorithm remainso efficient even on large instances and can serve to explore in practice the approximability of SLS.","PeriodicalId":9448,"journal":{"name":"Bulletin of the Society of Sea Water Science, Japan","volume":"2 1","pages":"18:1-18:14"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80047916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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