2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)最新文献_第2页

Data-driven relation discovery from unstructured texts 从非结构化文本中发现数据驱动关系

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005614205970602

Marilena Ditta, Fabrizio Milazzo, V. Ravì, G. Pilato, A. Augello

引用次数: 2

Use of frequent itemset mining techniques to analyze business processes 使用频繁的项目集挖掘技术来分析业务流程

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005598102730280

Vladimír Bartík, Milan Pospísil

引用次数: 2

The reverse doubling construction 反向加倍结构

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005613203500357

Jean-François Viaud, K. Bertet, C. Demko, R. Missaoui

引用次数: 6

Early diagnosis of Alzheimer's disease using machine learning techniques: A review paper 使用机器学习技术早期诊断阿尔茨海默病:综述论文

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005615203800387

Aunsia Khan, Muhammad Usman

{"title":"Early diagnosis of Alzheimer's disease using machine learning techniques: A review paper","authors":"Aunsia Khan, Muhammad Usman","doi":"10.5220/0005615203800387","DOIUrl":"https://doi.org/10.5220/0005615203800387","url":null,"abstract":"Alzheimer's, an irreparable brain disease, impairs thinking and memory while the aggregate mind size shrinks which at last prompts demise. Early diagnosis of AD is essential for the progress of more prevailing treatments. Machine learning (ML), a branch of artificial intelligence, employs a variety of probabilistic and optimization techniques that permits PCs to gain from vast and complex datasets. As a result, researchers focus on using machine learning frequently for diagnosis of early stages of AD. This paper presents a review, analysis and critical evaluation of the recent work done for the early detection of AD using ML techniques. Several methods achieved promising prediction accuracies, however they were evaluated on different pathologically unproven data sets from different imaging modalities making it difficult to make a fair comparison among them. Moreover, many other factors such as pre-processing, the number of important attributes for feature selection, class imbalance distinctively affect the assessment of the prediction accuracy. To overcome these limitations, a model is proposed which comprise of initial pre-processing step followed by imperative attributes selection and classification is achieved using association rule mining. Furthermore, this proposed model based approach gives the right direction for research in early diagnosis of AD and has the potential to distinguish AD from healthy controls.","PeriodicalId":102743,"journal":{"name":"2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)","volume":"42 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":"126829687","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: 45

Bringing search engines to the cloud using open source components 使用开源组件将搜索引擎带到云端

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005632701160126

K. Nagi

{"title":"Bringing search engines to the cloud using open source components","authors":"K. Nagi","doi":"10.5220/0005632701160126","DOIUrl":"https://doi.org/10.5220/0005632701160126","url":null,"abstract":"The usage of search engines is nowadays extended to do intelligent analytics of petabytes of data. With Lucene being at the heart of the vast majority of information retrieval systems, several attempts are made to bring it to the cloud in order to scale to big data. Efforts include implementing scalable distribution of the search indices over the file system, storing them in NoSQL databases, and porting them to inherently distributed ecosystems, such as Hadoop. We evaluate the existing efforts in terms of distribution, high availability, fault tolerance, manageability, and high performance. We believe that the key to supporting search indexing capabilities for big data can only be achieved through the use of common open-source technology to be deployed on standard cloud platforms such as Amazon EC2, Microsoft Azure, etc. For each approach, we build a benchmarking system by indexing the whole Wikipedia content and submitting hundreds of simultaneous search requests. We measure the performance of both indexing and searching operations. We stimulate node failures and monitor the recoverability of the system. We show that a system built on top of Solr and Hadoop has the best stability and manageability; while systems based on NoSQL databases present an attractive alternative in terms of performance.","PeriodicalId":102743,"journal":{"name":"2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)","volume":"11 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":"116556357","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

Automatic extraction of task statements from structured meeting content 从结构化会议内容中自动提取任务语句

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005609703070315

K. Nagao, Keisuke Inoue, Naoya Morita, S. Matsubara

引用次数: 10

Arabic sentiment analysis using WEKA a hybrid learning approach 阿拉伯语情感分析使用WEKA混合学习方法

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005616004020408

S. Alhumoud, Tarfa Albuhairi, Mawaheb Altuwaijri

引用次数: 23

Comparison of sampling size estimation techniques for association rule mining 关联规则挖掘中采样大小估计技术的比较

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005589801950202

Tugba Halici, U. Ketenci

引用次数: 1

Piecewise Chebyshev factorization based nearest neighbour classification for time series 基于分段切比雪夫分解的时间序列最近邻分类

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005611900840091

Qinglin Cai, Ling Chen, Jianling Sun

{"title":"Piecewise Chebyshev factorization based nearest neighbour classification for time series","authors":"Qinglin Cai, Ling Chen, Jianling Sun","doi":"10.5220/0005611900840091","DOIUrl":"https://doi.org/10.5220/0005611900840091","url":null,"abstract":"In the research field of time series analysis and mining, the nearest neighbour classifier (1NN) based on dynamic time warping distance (DTW) is well known for its high accuracy. However, the high computational complexity of DTW can lead to the expensive time consumption of classification. An effective solution is to compute DTW in the piecewise approximation space (PA-DTW), which transforms the raw data into the feature space based on segmentation, and extracts the discriminatory features for similarity measure. However, most of existing piecewise approximation methods need to fix the segment length, and focus on the simple statistical features, which would influence the precision of PA-DTW. To address this problem, we propose a novel piecewise factorization model for time series, which uses an adaptive segmentation method and factorizes the subsequences with the Chebyshev polynomials. The Chebyshev coefficients are extracted as features for PA-DTW measure (ChebyDTW), which are able to capture the fluctuation information of time series. The comprehensive experimental results show that ChebyDTW can support the accurate and fast 1NN classification.","PeriodicalId":102743,"journal":{"name":"2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)","volume":"46 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":"132110828","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

Word Sense Discrimination on tweets: A graph-based approach 推文的词义辨析:基于图的方法

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005640501380146

F. M. Cecchini, E. Fersini, E. Messina

引用次数: 6