2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)最新文献_第4页

Real-time prediction to support decision-making in soccer 实时预测，支持足球决策

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005595302180225

Yasuo Saito, M. Kimura, S. Ishizaki

引用次数: 4

Real-time local topic extraction using density-based adaptive spatiotemporal clustering for enhancing local situation awareness 基于密度的自适应时空聚类实时局部主题提取，增强局部态势感知

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005593302030210

Tatsuhiro Sakai, Keiichi Tamura, Shota Kotozaki, Tsubasa Hayashida, H. Kitakami

{"title":"Real-time local topic extraction using density-based adaptive spatiotemporal clustering for enhancing local situation awareness","authors":"Tatsuhiro Sakai, Keiichi Tamura, Shota Kotozaki, Tsubasa Hayashida, H. Kitakami","doi":"10.5220/0005593302030210","DOIUrl":"https://doi.org/10.5220/0005593302030210","url":null,"abstract":"In the era of big data, we are witnessing the rapid growth of a new type of information source. In particular, tweets are one of the most widely used microblogging services for situation awareness during emergencies. In our previous work, we focused on geotagged tweets posted on Twitter that included location information as well as a time and text message. We previously developed a real-time analysis system using the (ε,τ)-density-based adaptive spatiotemporal clustering algorithm to analyze local topics and events. The proposed spatiotemporal analysis system successfully detects emerging bursty areas in which geotagged tweets related to observed topics are posted actively; however the system is tailor-made and specialized for a particular observed topic, therefore, it cannot identify other topics. To address this issue, we propose a new real-time spatiotemporal analysis system for enhancing local situation awareness using a density-based adaptive spatiotemporal clustering algorithm. In the proposed system, local bursty keywords are extracted and their bursty areas are identified. We evaluated the proposed system using actual real world topics related to weather in Japan. Experimental results show that the proposed system can extract local topics and events.","PeriodicalId":102743,"journal":{"name":"2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115531926","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

Data driven structural similarity: A Distance measure for adaptive linear approximations of time series 数据驱动的结构相似性:时间序列自适应线性逼近的距离度量

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005597400670074

V. Ionescu, R. Potolea, M. Dînsoreanu

{"title":"Data driven structural similarity: A Distance measure for adaptive linear approximations of time series","authors":"V. Ionescu, R. Potolea, M. Dînsoreanu","doi":"10.5220/0005597400670074","DOIUrl":"https://doi.org/10.5220/0005597400670074","url":null,"abstract":"Much effort has been invested in recent years in the problem of detecting similarity in time series. Most work focuses on the identification of exact matches through point-by-point comparisons, although in many real-world problems recurring patterns match each other only approximately. We introduce a new approach for identifying patterns in time series, which evaluates the similarity by comparing the overall structure of candidate sequences instead of focusing on the local shapes of the sequence and propose a new distance measure ABC (Area Between Curves) that is used to achieve this goal. The approach is based on a data-driven linear approximation method that is intuitive, offers a high compression ratio and adapts to the overall shape of the sequence. The similarity of candidate sequences is quantified by means of the novel distance measure, applied directly to the linear approximation of the time series. Our evaluations performed on multiple data sets show that our proposed technique outperforms similarity search approaches based on the commonly referenced Euclidean Distance in the majority of cases. The most significant improvements are obtained when applying our method to domains and data sets where matching sequences are indeed primarily determined based on the similarity of their higher-level structures.","PeriodicalId":102743,"journal":{"name":"2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129419285","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

Motive-based search: Computing regions from large knowledge bases using geospatial coordinates 基于动机的搜索:使用地理空间坐标从大型知识库计算区域

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005635004690474

L. Avdiyenko, Martin Nettling, Christian Lemke, Matthias Wauer, A. N. Ngomo, A. Both

{"title":"Motive-based search: Computing regions from large knowledge bases using geospatial coordinates","authors":"L. Avdiyenko, Martin Nettling, Christian Lemke, Matthias Wauer, A. N. Ngomo, A. Both","doi":"10.5220/0005635004690474","DOIUrl":"https://doi.org/10.5220/0005635004690474","url":null,"abstract":"To create a better search experience for end users and to satisfy their actual intents even for vaguely formulated queries, a contemporary search engine has to go beyond simple keyword-based retrieval concepts. For a geospatial search, where user queries can be quite complex such as “places for winter sport holidays and culture in Central Europe”, we introduce the notion of geospatial motifs denoting traits of geographical regions. Defining a motif by a set of geospatial entities with certain characteristics, we present an approach to inferring important regions for the motif based on density of these entities. The evaluation of the approach for several motifs showed that the inferred regions are among the most popular places for a motif of interest according to the opinion of several experts and official rankings. Thus, we claim that the presented semi-automatic process of detecting regions for geospatial motifs can contribute to more powerful and flexible search applications which are able to answer user queries containing complex geospatial concepts.","PeriodicalId":102743,"journal":{"name":"2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)","volume":"291 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116176447","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: 0

Interesting regression- and model trees through variable restrictions 有趣的回归和模型树通过变量的限制

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005600302810292

Rikard König, U. Johansson, A. Lindqvist, Peter Brattberg

{"title":"Interesting regression- and model trees through variable restrictions","authors":"Rikard König, U. Johansson, A. Lindqvist, Peter Brattberg","doi":"10.5220/0005600302810292","DOIUrl":"https://doi.org/10.5220/0005600302810292","url":null,"abstract":"The overall purpose of this paper is to suggest a new technique for creating interesting regression- and model trees. Interesting models are here defined as models that fulfill some domain dependent restriction of how variables can be used in the models. The suggested technique, named ReReM, is an extension of M5 which can enforce variable constraints while creating regression and model trees. To evaluate ReReM, two case studies were conducted where the first concerned modeling of golf player skill, and the second modeling of fuel consumption in trucks. Both case studies had variable constraints, defined by domain experts, that should be fulfilled for models to be deemed interesting. When used for modeling golf player skill, ReReM created regression trees that were slightly less accurate than M5s regression trees. However, the models created with ReReM were deemed to be interesting by a golf teaching professional while the M5 models were not. In the second case study, ReReM was evaluated against M5s model trees and a semi-automated approach often used in the automotive industry. Here, experiments showed that ReReM could achieve a predictive performance comparable to M5 and clearly better than a semi-automated approach, while fulfilling the constraints regarding interesting models.","PeriodicalId":102743,"journal":{"name":"2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128126371","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: 0

Optimizing dependency parsing throughput 优化依赖项解析吞吐量

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005638905110516

A. Weichselbraun, Norman Süsstrunk

引用次数: 5

Aftermath of 2008 financial crisis on oil prices 2008年金融危机对油价的影响

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005596902350240

N. Sehgal, Krishan Kumar Pandey

{"title":"Aftermath of 2008 financial crisis on oil prices","authors":"N. Sehgal, Krishan Kumar Pandey","doi":"10.5220/0005596902350240","DOIUrl":"https://doi.org/10.5220/0005596902350240","url":null,"abstract":"Geopolitical and economic events had strong impact on crude oil markets for over 40 years. Oil prices steadily rose for several years and in July 2008 stood at a record high of $145 per barrel. Further, it plunged to $43 per barrel by end of 2008. There is need to identify appropriate features (factors) explaining the characteristics of oil markets during booming and downturn period. Feature selection can help in identifying the most informative and influential input variables before and after financial crisis. The study used an extended version of MI3 algorithm i.e. I2MI2 algorithm together with general regression neural network as forecasting engine to examine the explanatory power of selected features and their contribution in driving oil prices. The study used features selected from proposed methodology for one-month ahead and twelve-month ahead forecast horizon. The forecast from the proposed methodology outperformed in comparison to EIA's STEO estimates. Results shows that reserves and speculations were main players before the crisis and the overall mechanism was broken due to 2008 global financial crisis. The contribution of emerging economy (China) emerged as important variable in explaining the directions of oil prices. EPPI and CPI remain the building blocks before and after crisis while influence of Non-OECD consumption rises after the crisis.","PeriodicalId":102743,"journal":{"name":"2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)","volume":"214 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134506207","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

Clustering stability and ground truth: Numerical experiments 聚类稳定性和地面真值:数值实验

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005597702590264

M. Amorim, Margarida M. G. S. Cardoso

引用次数: 1

qRead: A fast and accurate article extraction method from web pages using partition features optimizations qRead:使用分区特征优化从网页中快速准确地提取文章的方法

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005613603640371

Jingwen Wang, Jie Wang

{"title":"qRead: A fast and accurate article extraction method from web pages using partition features optimizations","authors":"Jingwen Wang, Jie Wang","doi":"10.5220/0005613603640371","DOIUrl":"https://doi.org/10.5220/0005613603640371","url":null,"abstract":"We present a new method called qRead to achieve real-time content extractions from web pages with high accuracy. Early approaches to content extractions include empirical filtering rules, Document Object Model (DOM) trees, and machine learning models. These methods, while having met with certain success, may not meet the requirements of real-time extraction with high accuracy. For example, constructing a DOM-tree on a complex web page is time-consuming, and using machine learning models could make things unnecessarily more complicated. Different from previous approaches, qRead uses segment densities and similarities to identify main contents. In particular, qRead first filters obvious junk contents, eliminates HTML tags, and partitions the remaining text into natural segments. It then uses the highest ratio of words over the number of lines in a segment combined with similarity between the segment and the title to identify main contents. We show that, through extensive experiments, qRead achieves a 96.8% accuracy on Chinese web pages with an average extraction time of 13.20 milliseconds, and a 93.6% accuracy on English web pages with an average extraction time of 11.37 milliseconds, providing substantial improvements on accuracy over previous approaches and meeting the real-time extraction requirement.","PeriodicalId":102743,"journal":{"name":"2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132873556","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

Learning text patterns to detect opinion targets 学习文本模式来检测意见目标

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005612603370343

Filipa Peleja, João Magalhães

引用次数: 2