Proceedings of the 32nd International Conference on Scientific and Statistical Database Management最新文献

A Decomposition Approach for Risk-Averse Index Selection 风险规避指数选择的分解方法

Proceedings of the 32nd International Conference on Scientific and Statistical Database Management Pub Date : 2020-07-07 DOI: 10.1145/3400903.3400909

R. Schlosser, Stefan Halfpap

引用次数: 6

LRZ Convolution: An Algorithm for Automatic Anomaly Detection in Time-series Data LRZ卷积:一种时间序列数据自动异常检测算法

Proceedings of the 32nd International Conference on Scientific and Statistical Database Management Pub Date : 2020-07-07 DOI: 10.1145/3400903.3400904

A. Marathe

引用次数: 1

Efficient Search over Genomic Short Read Data 基因组短读数据的高效搜索

Proceedings of the 32nd International Conference on Scientific and Statistical Database Management Pub Date : 2020-07-07 DOI: 10.1145/3400903.3400907

Wangda Zhang, Mengdi Lin, K. A. Ross

{"title":"Efficient Search over Genomic Short Read Data","authors":"Wangda Zhang, Mengdi Lin, K. A. Ross","doi":"10.1145/3400903.3400907","DOIUrl":"https://doi.org/10.1145/3400903.3400907","url":null,"abstract":"Modern DNA sequencing technology produces large volumes of genome strings for various biological and medical applications. To mitigate the space overhead required for storing these genome data, previous research has developed compression schemes to save storage resources and to improve the locality for bioinformatics applications. These approaches, however, typically need significant additional processing to support efficient genome string search, an important step for downstream applications in a bioinformatics pipeline. In this paper, we propose to store raw DNA sequence data in a compressed but searchable format, enabling efficient string lookups using database indexes. To build this format, we partition genome strings by computing hash-based minimizers to group overlapping strings together. We carefully optimize our hash function to avoid large buckets caused by repetitive sequences. These buckets are then effectively compressed using local constructed references, and an index is built upon them to guide exact match lookups. Both data compression and string lookups exploit the enhanced locality as a result of the partitioning. We implement storage and search functions over the storage format as a multithreaded library, and perform extensive experiments on real genome sequencing data. The results show that our approach efficiently executes genome string lookups over highly compressed raw read data.","PeriodicalId":380478,"journal":{"name":"Proceedings of the 32nd International Conference on Scientific and Statistical Database Management","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127328789","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}

引用次数: 1

Multidimensional Clustering over Big Data: Models, Issues, Analysis, Emerging Trends 多维聚类在大数据:模型，问题，分析，新兴趋势

Proceedings of the 32nd International Conference on Scientific and Statistical Database Management Pub Date : 2020-07-07 DOI: 10.1145/3400903.3409117

A. Cuzzocrea

{"title":"Multidimensional Clustering over Big Data: Models, Issues, Analysis, Emerging Trends","authors":"A. Cuzzocrea","doi":"10.1145/3400903.3409117","DOIUrl":"https://doi.org/10.1145/3400903.3409117","url":null,"abstract":"Clustering is an essential task of the whole pattern recognition process, and it can serve under several roles, for instance in terms of data pre-processing tool for better (i.e., more accurate) pattern recognition analysis and mining. In this vest, a critical applicative setting is represented by applying pattern recognition tools over emerging big data. Here, clustering specially plays a challenging role within the context of this conceptual mining framework, and, under a larger vision, it can act as pre-processing task for general big data clustering problems. In this paper, we first focus on state-of-the-art solutions for big data clustering in the specific pattern recognition context, by highlighting benefits and limitations. Then, we focus the attention on the problem of effectively and efficiently clustering big data via innovative multidimensional metaphors, thus achieving the definition of so-called multidimensional clustering over big data. In this so-delineated research setting, based on the well-known challenges of big data management (e.g., volume, velocity, variety, and veracity), we provide critical review and discussion, complemented by a rich set of research directions, development perspectives and emerging trends of the investigated topics, as contextualized in the reference big-data-analytics scientific area.","PeriodicalId":380478,"journal":{"name":"Proceedings of the 32nd International Conference on Scientific and Statistical Database Management","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116489127","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}

引用次数: 2

Figure Captioning in Scholarly Literatures to Augment Search Results 在学术文献中添加图形标题以增强搜索结果

Proceedings of the 32nd International Conference on Scientific and Statistical Database Management Pub Date : 2020-07-07 DOI: 10.1145/3400903.3400906

Jingya Yang, Dongdong Zhang, Gaocai Dong, Jing Peng

{"title":"Figure Captioning in Scholarly Literatures to Augment Search Results","authors":"Jingya Yang, Dongdong Zhang, Gaocai Dong, Jing Peng","doi":"10.1145/3400903.3400906","DOIUrl":"https://doi.org/10.1145/3400903.3400906","url":null,"abstract":"Figures convey useful information, such as trends, proportions, and values, in a concise format. People can understand these attributes at a glance, but machine process them difficultly. When searching for figures, the end-user is presented with the caption that does not contain enough information to interpret the figure. In the paper, we propose a novel end-to-end framework for scholarly figure captioning. In the figure parsing module, figures are localized, classified, and analyzed. The plotted data and its association with the legend entries are extracted. In text processing module, the figure-related sentences are identified and measured with the sentence’s relevance to the figure. The sentence subset with the optimum size is selected considering a balance between information content and the size of the generated caption. The final complete captions enable a variety of current exciting applications, such as figure search engine and figure query answering. Empirical experiments show that our proposed framework can effectively generate captions for figures under several metrics.","PeriodicalId":380478,"journal":{"name":"Proceedings of the 32nd International Conference on Scientific and Statistical Database Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126067515","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}

引用次数: 1

Proceedings of the 32nd International Conference on Scientific and Statistical Database Management 第32届科学与统计数据库管理国际会议论文集

Proceedings of the 32nd International Conference on Scientific and Statistical Database Management Pub Date : 2020-07-07 DOI: 10.1145/3400903

引用次数: 1

GeoPrune: Efficiently Matching Trips in Ride-sharing Through Geometric Properties georune:利用几何属性有效匹配拼车出行

Proceedings of the 32nd International Conference on Scientific and Statistical Database Management Pub Date : 2020-07-07 DOI: 10.1145/3400903.3400912

Yixin Xu, Jianzhong Qi, Renata Borovica-Gajic, L. Kulik

引用次数: 3

Freedom for the SQL-Lambda: Just-in-Time-Compiling User-Injected Functions in PostgreSQL 自由的SQL-Lambda:即时编译PostgreSQL中的用户注入函数

Proceedings of the 32nd International Conference on Scientific and Statistical Database Management Pub Date : 2020-07-07 DOI: 10.1145/3400903.3400915

Maximilian E. Schüle, Jakob Huber, A. Kemper, Thomas Neumann

{"title":"Freedom for the SQL-Lambda: Just-in-Time-Compiling User-Injected Functions in PostgreSQL","authors":"Maximilian E. Schüle, Jakob Huber, A. Kemper, Thomas Neumann","doi":"10.1145/3400903.3400915","DOIUrl":"https://doi.org/10.1145/3400903.3400915","url":null,"abstract":"As part of the code-generating database system HyPer, SQL lambda functions allow user-defined metrics to be injected into data mining operators during compile time. Since version 11, PostgreSQL has supported just-in-time compilation with LLVM for expression evaluation. This enables the concept of SQL lambda functions to be transferred to this open-source database system. In this study, we extend PostgreSQL by adding two subquery types for lambda expressions that either pre-materialise the result or return a cursor to request tuples. We demonstrate the usage of these subquery types in conjunction with dedicated table functions for data mining algorithms such as PageRank, k-Means clustering and labelling. Furthermore, we allow four levels of optimisation for query execution, ranging from interpreted function calls to just-in-time-compiled execution. The latter—with some adjustments to the PostgreSQL’s execution engine—transforms our lambda functions into real user-injected code. In our evaluation with the LDBC social network benchmark for PageRank and the Chicago taxi data set for clustering, optimised lambda functions achieved comparable performance to hard-coded implementations and HyPer’s data mining algorithms.","PeriodicalId":380478,"journal":{"name":"Proceedings of the 32nd International Conference on Scientific and Statistical Database Management","volume":"361 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122828723","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}

引用次数: 17

STILT: Unifying Spatial, Temporal and Textual Search using a Generalized Multi-dimensional Index 使用广义多维索引统一空间、时间和文本搜索

Proceedings of the 32nd International Conference on Scientific and Statistical Database Management Pub Date : 2020-07-07 DOI: 10.1145/3400903.3400927

Yoann Arseneau, Saransh Gautam, B. Nickerson, S. Ray

{"title":"STILT: Unifying Spatial, Temporal and Textual Search using a Generalized Multi-dimensional Index","authors":"Yoann Arseneau, Saransh Gautam, B. Nickerson, S. Ray","doi":"10.1145/3400903.3400927","DOIUrl":"https://doi.org/10.1145/3400903.3400927","url":null,"abstract":"The proliferation of location-enabled sensors, smart phones, and the power of digital messaging combined with social media platforms is producing a deluge of multi-dimensional data. Novel index structures are needed to efficiently process massive amounts of geo-tagged data, and to promptly answer queries with textual, spatial, and temporal components. Existing approaches to spatio-temporal text indexing lack a unified index supporting efficient range and top-k search on any combination of location, time, or text. We introduce a generalized, multi-dimensional index called Spatio-temporal Textual InterLeaved Trie (STILT), which unifies spatial, textual, and temporal components within a single structure. STILT is a general-purpose index supporting subset searches (e.g. spatio-textual, spatio-temporal, spatial, textual), as well as full spatio-temporal textual searches. STILT uses a binary trie-based index interleaving text, location, and time information in a space-efficient manner. STILT supports parallel building of the index and concurrent execution of spatio-temporal textual queries, including top-k and range search queries. With extensive evaluation, we demonstrate that STILT is significantly faster than state-of-the-art approaches in terms of index construction time and search latency.","PeriodicalId":380478,"journal":{"name":"Proceedings of the 32nd International Conference on Scientific and Statistical Database Management","volume":"351 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114000471","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}

引用次数: 4

Unsupervised non-parametric change point detection in electrocardiography 心电图无监督非参数变化点检测

Proceedings of the 32nd International Conference on Scientific and Statistical Database Management Pub Date : 2020-02-07 DOI: 10.1145/3400903.3400917

N. Shvetsov, N. Buzun, D. Dylov

引用次数: 7