Proceedings of the 27th International Conference on Scientific and Statistical Database Management最新文献

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Proceedings of the 27th International Conference on Scientific and Statistical Database Management Pub Date : 2015-06-29 DOI: 10.1145/2791347.2791361

Gangyi Zhu, Yi Wang, G. Agrawal

引用次数: 20

A compression-based framework for the efficient analysis of business process logs 用于有效分析业务流程日志的基于压缩的框架

Proceedings of the 27th International Conference on Scientific and Statistical Database Management Pub Date : 2015-06-29 DOI: 10.1145/2791347.2791351

Bettina Fazzinga, S. Flesca, F. Furfaro, E. Masciari, L. Pontieri

{"title":"A compression-based framework for the efficient analysis of business process logs","authors":"Bettina Fazzinga, S. Flesca, F. Furfaro, E. Masciari, L. Pontieri","doi":"10.1145/2791347.2791351","DOIUrl":"https://doi.org/10.1145/2791347.2791351","url":null,"abstract":"The increasing availability of large process log repositories calls for efficient solutions for their analysis. In this regard, a novel specialized compression technique for process logs is proposed, that builds a synopsis supporting a fast estimation of aggregate queries, which are of crucial importance in exploratory and high-level analysis tasks. The synopsis is constructed by progressively merging the original log-tuples, which represent single activity executions within the process instances, into aggregate tuples, summarizing sets of activity executions. The compression strategy is guided by a heuristic aiming at limiting the loss of information caused by summarization, while guaranteeing that no information is lost on the set of activities performed within the process instances and on the order among their executions. The selection conditions in an aggregate query are specified in terms of a graph pattern, that allows precedence relationships over activity executions to be expressed, along with conditions on their starting times, durations, and executors. The efficacy of the compression technique, in terms of capability of reducing the size of the log and of accuracy of the estimates retrieved from the synopsis, has been experimentally validated.","PeriodicalId":225179,"journal":{"name":"Proceedings of the 27th International Conference on Scientific and Statistical Database Management","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129795312","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

TarMiner: automatic extraction of miRNA targets from literature TarMiner:从文献中自动提取miRNA目标

Proceedings of the 27th International Conference on Scientific and Statistical Database Management Pub Date : 2015-06-29 DOI: 10.1145/2791347.2791366

R. Tsoupidi, Ilias Kanellos, Thanasis Vergoulis, I. Vlachos, A. Hatzigeorgiou, Theodore Dalamagas

引用次数: 2

GEN: a database interface generator for HPC programs GEN:用于HPC程序的数据库接口生成器

Proceedings of the 27th International Conference on Scientific and Statistical Database Management Pub Date : 2015-06-29 DOI: 10.1145/2791347.2791363

Quan Pham, T. Malik

引用次数: 0

How to quantify the impact of lossy transformations on change detection 如何量化有损转换对变更检测的影响

Proceedings of the 27th International Conference on Scientific and Statistical Database Management Pub Date : 2015-06-29 DOI: 10.1145/2791347.2791371

Pavel Efros, Erik Buchmann, Adrian Englhardt, Klemens Böhm

引用次数: 3

Compact distance histogram: a novel structure to boost k-nearest neighbor queries 紧凑距离直方图:一种提高k近邻查询的新结构

Proceedings of the 27th International Conference on Scientific and Statistical Database Management Pub Date : 2015-06-29 DOI: 10.1145/2791347.2791359

M. Bedo, D. S. Kaster, A. Traina, C. Traina

{"title":"Compact distance histogram: a novel structure to boost k-nearest neighbor queries","authors":"M. Bedo, D. S. Kaster, A. Traina, C. Traina","doi":"10.1145/2791347.2791359","DOIUrl":"https://doi.org/10.1145/2791347.2791359","url":null,"abstract":"The k-Nearest Neighbor query (k-NNq) is one of the most useful similarity queries. Elaborated k-NNq algorithms depend on an initial radius to prune regions of the search space that cannot contribute to the answer. Therefore, estimating a suitable starting radius is of major importance to accelerate k-NNq execution. This paper presents a new technique to estimate a tight initial radius. Our approach, named CDH-kNN, relies on Compact Distance Histograms (CDHs), which are pivot-based histograms defined as piecewise linear functions. Such structures approximate the distance distribution and are compressed according to a given constraint, which can be a desired number of buckets and/or a maximum allowed error. The covering radius of a k-NNq is estimated based on the relationship between the query element and the CDHs' joint frequencies. The paper presents a complete specification of CDH-kNN, including CDH's construction and radii estimation. Extensive experiments on both real and synthetic datasets highlighted the efficiency of our approach, showing that it was up to 72% faster than existing algorithms, outperforming every competitor in all the setups evaluated. In fact, the experiments showed that our proposal was just 20% slower than the theoretical lower bound.","PeriodicalId":225179,"journal":{"name":"Proceedings of the 27th International Conference on Scientific and Statistical Database Management","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121706330","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

Batch matching of conjunctive triple patterns over linked data streams in the internet of things 物联网中关联数据流上连接三重模式的批量匹配

Proceedings of the 27th International Conference on Scientific and Statistical Database Management Pub Date : 2015-06-29 DOI: 10.1145/2791347.2791364

Yongrui Qin, Quan Z. Sheng, Nickolas J. G. Falkner, A. Shemshadi, E. Curry

引用次数: 2

Towards automated prediction of relationships among scientific datasets 迈向科学数据集之间关系的自动预测

Proceedings of the 27th International Conference on Scientific and Statistical Database Management Pub Date : 2015-06-29 DOI: 10.1145/2791347.2791358

Abdussalam Alawini, D. Maier, K. Tufte, Bill Howe, Rashmi Nandikur

{"title":"Towards automated prediction of relationships among scientific datasets","authors":"Abdussalam Alawini, D. Maier, K. Tufte, Bill Howe, Rashmi Nandikur","doi":"10.1145/2791347.2791358","DOIUrl":"https://doi.org/10.1145/2791347.2791358","url":null,"abstract":"Before scientists can analyze, publish, or share their data, they often need to determine how their datasets are related. Determining relationships helps scientists identify the most complete version of a dataset, detect versions of datasets that complement each other, and determine multiple datasets that overlap. In previous work, we showed how observable relationships between two datasets help scientists recall their original derivation connection. While that work helped with identifying relationships between two datasets, it is infeasible for scientists to use it for finding relationships between all possible pairs in a large collection of datasets. In order to deal with larger numbers of datasets, we are extending our methodology with a relationship-prediction system, ReDiscover, a tool to identify pairs from a collection of datasets that are most likely related and the relationship between them. We report on the initial design of ReDiscover, which uses machine-learning methods such as Conditional Random Fields and Support Vector Machines to the relationship-discovery problem. Our preliminarily evaluation shows that ReDiscover predicted relationships with an average accuracy of 87%.","PeriodicalId":225179,"journal":{"name":"Proceedings of the 27th International Conference on Scientific and Statistical Database Management","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129209613","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

RUBIK: efficient threshold queries on massive time series RUBIK:大规模时间序列的有效阈值查询

Proceedings of the 27th International Conference on Scientific and Statistical Database Management Pub Date : 2015-06-29 DOI: 10.1145/2791347.2791372

Eleni Tzirita Zacharatou, F. Tauheed, T. Heinis, A. Ailamaki

{"title":"RUBIK: efficient threshold queries on massive time series","authors":"Eleni Tzirita Zacharatou, F. Tauheed, T. Heinis, A. Ailamaki","doi":"10.1145/2791347.2791372","DOIUrl":"https://doi.org/10.1145/2791347.2791372","url":null,"abstract":"An increasing number of applications from finance, meteorology, science and others are producing time series as output. The analysis of the vast amount of time series is key to understand the phenomena studied, particularly in the simulation sciences, where the analysis of time series resulting from simulation allows scientists to refine the model simulated. Existing approaches to query time series typically keep a compact representation in main memory, use it to answer queries approximately and then access the exact time series data on disk to validate the result. The more precise the in-memory representation, the fewer disk accesses are needed to validate the result. With the massive sizes of today's datasets, however, current in-memory representations oftentimes no longer fit into main memory. To make them fit, their precision has to be reduced considerably resulting in substantial disk access which impedes query execution today and limits scalability for even bigger datasets in the future. In this paper we develop RUBIK, a novel approach to compressing and indexing time series. RUBIK exploits that time series in many applications and particularly in the simulation sciences are similar to each other. It compresses similar time series, i.e., observation values as well as time information, achieving better space efficiency and improved precision. RUBIK translates threshold queries into two dimensional spatial queries and efficiently executes them on the compressed time series by exploiting the pruning power of a tree structure to find the result, thereby outperforming the state-of-the-art by a factor of between 6 and 23. As our experiments further indicate, exploiting similarity within and between time series is crucial to make query execution scale and to ultimately decouple query execution time from the growth of the data (size and number of time series).","PeriodicalId":225179,"journal":{"name":"Proceedings of the 27th International Conference on Scientific and Statistical Database Management","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116657797","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: 6

Data mining for isotopic mapping of bioarchaeological finds in a central european alpine passage 中欧阿尔卑斯通道生物考古发现同位素制图的数据挖掘

Proceedings of the 27th International Conference on Scientific and Statistical Database Management Pub Date : 2015-06-29 DOI: 10.1145/2791347.2791357

Markus Mauder, Eirini Ntoutsi, Peer Kröger, G. Grupe

引用次数: 5