2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)最新文献_第4页

Resolving the message riddle: A multi-pronged approach to infer trouble tickets 解决消息之谜:一种多管齐下的方法来推断故障单

2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2021-10-06 DOI: 10.1109/DSAA53316.2021.9564152

Vikrant Shimpi, Aakash Patel, Siddartha Kshirsagar, M. Natu, Monika Bhave

引用次数: 0

Leveraging Latent Economic Concepts and Sentiments in the News for Market Prediction 利用新闻中潜在的经济概念和情绪进行市场预测

2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2021-10-06 DOI: 10.1109/DSAA53316.2021.9564122

Saeede Anbaee Farimani, M. V. Jahan, A. M. Fard, Gholamreza Haffari

{"title":"Leveraging Latent Economic Concepts and Sentiments in the News for Market Prediction","authors":"Saeede Anbaee Farimani, M. V. Jahan, A. M. Fard, Gholamreza Haffari","doi":"10.1109/DSAA53316.2021.9564122","DOIUrl":"https://doi.org/10.1109/DSAA53316.2021.9564122","url":null,"abstract":"Most of the existing news-based market prediction techniques disregard conceptual and emotional relations in the news stream. In this work, we consider the conceptual relationship between news documents using contextualized latent concept modeling as well as leveraging news sentiment and technical indicators. We present our approach as an open-source RESTFul API. We build a corpus of financial news related to currency pairs in the Foreign Exchange and Cryptocurrencies markets. Next, we apply BERT-based embedding to generate word vectors, cluster the vectors to create latent economic concepts, and propose a document representation based on the distribution of words on these concepts as well as news sentiment. We use a recurrent convolutional neural network to jointly use BERT-based text representation and technical indicators embedding for market time series prediction. We further augment our model with technical indicators using another recurrent layer. The experimental results show the superiority of our method compared to the baselines. Our MarketNews dataset, news crawler, and MarketPredict APIs are available for public use.","PeriodicalId":129612,"journal":{"name":"2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125258613","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

motif2vec: Semantic-aware Representation Learning for Wearables' Time Series Data motif2vec:可穿戴设备时间序列数据的语义感知表示学习

2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2021-10-06 DOI: 10.1109/DSAA53316.2021.9564120

Suwen Lin, Xian Wu, N. Chawla

{"title":"motif2vec: Semantic-aware Representation Learning for Wearables' Time Series Data","authors":"Suwen Lin, Xian Wu, N. Chawla","doi":"10.1109/DSAA53316.2021.9564120","DOIUrl":"https://doi.org/10.1109/DSAA53316.2021.9564120","url":null,"abstract":"The proliferation of wearable sensors allows for the continuous collection of temporal characterization of an individual's physical activity and physiological data. This is enabling an unprecedented opportunity to delve into a deeper analysis of the underlying patterns of such temporal data and to infer attributes associated with health, behaviors, and well-being. However, there remain several challenges to fully discover both structural and temporal patterns (motifs) in these data streams and to leverage the semantic relationship among these motifs. These include: i) the temporal data of variable length and high resolution leads to the motifs of various sizes; ii) periodic occurrences and hierarchical overlaps of these motifs further challenge the modeling of their complex structural and semantic relations. We propose a semantic-aware unsupervised representation learning model, motif2vec, to learn the latent representation of time series data collected from wearable sensors. The motif2vec consists of three major components: 1) transforming the time series into a set of variable-length motif sequences; 2) formalizing random walks to construct the neighborhood of motifs and thus to extract structural and semantic relationship among motifs; 3) learning time series latent features to capture the motif neighborhood structure with a skip-gram model. Experiments on two real-world datasets, derived from two different wearables and population groups, show motif2vec outperforms six state-of-the-art benchmarks on various tasks.","PeriodicalId":129612,"journal":{"name":"2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123652579","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

An Overview on Supervised Semi-structured Data Classification 有监督半结构化数据分类综述

2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2021-10-06 DOI: 10.1109/DSAA53316.2021.9564205

Lijun Zhang, Ning Li, Zhanhuai Li

{"title":"An Overview on Supervised Semi-structured Data Classification","authors":"Lijun Zhang, Ning Li, Zhanhuai Li","doi":"10.1109/DSAA53316.2021.9564205","DOIUrl":"https://doi.org/10.1109/DSAA53316.2021.9564205","url":null,"abstract":"Many collaboratively building resources, such as Wikipedia, Weibo and Quora, exist in the form of semi-structured data. The semi-structured data has been widely used in areas such as data integration, data distribution, data storage, data management, information retrieval and knowledge management. For large volumes of semi-structured data on the Web, semi-structured data classification technique can group them into different categories by their structure and/or content information. Supervised semi-structured data classification plays an important role in many applications. This paper provides an overview of the literature in the area of supervised semi-structured data classification. A general framework for semi-structured data classification is presented, which is mainly composed of two steps: feature extraction and model building. Several different representation models of semi-structured data are discussed, mainly including rooted labeled tree model, feature vector space model and feature set model. A large selection of semi-structured data classification approaches are reviewed in detail from two aspects: based on structure only and based on both structure and content. Finally, several future research directions for semistructured data classification are presented.","PeriodicalId":129612,"journal":{"name":"2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129634621","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

Migrate On-Premises Real-Time Data Analytics Jobs Into the Cloud 将本地实时数据分析工作迁移到云中

2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2021-10-06 DOI: 10.1109/DSAA53316.2021.9564177

Huijun Wu, Xiaoyao Qian, Hulya Pamukcu Crowell, Tushar Singh, Aleks Shulman, Prashil Bhimani, Abhishek Maloo, Chunxu Tang, Yao Li, Lu Zhang, Chris Ulherr

引用次数: 4

Source detection on networks using spatial temporal graph convolutional networks 基于时空图卷积网络的网络源检测

2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2021-10-06 DOI: 10.1109/DSAA53316.2021.9564188

Hao Sha, M. Hasan, G. Mohler

引用次数: 0

CAUSALYSIS: Causal Machine Learning for Real-Estate Investment Decisions 因果分析:房地产投资决策的因果机器学习

2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2021-10-06 DOI: 10.1109/DSAA53316.2021.9564210

Rodrigo Rivera-Castro, Evgeny Burnaev

引用次数: 0

PEAR: A Product Expiry-Aware and Revenue-Conscious Itemset Placement Scheme PEAR:产品到期意识和收益意识的项目集放置方案

2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2021-10-06 DOI: 10.1109/DSAA53316.2021.9564189

Anirban Mondal, Raghav Mittal, Vrinda Khandelwal, Parul Chaudhary, P. Reddy

{"title":"PEAR: A Product Expiry-Aware and Revenue-Conscious Itemset Placement Scheme","authors":"Anirban Mondal, Raghav Mittal, Vrinda Khandelwal, Parul Chaudhary, P. Reddy","doi":"10.1109/DSAA53316.2021.9564189","DOIUrl":"https://doi.org/10.1109/DSAA53316.2021.9564189","url":null,"abstract":"Placement of items on the shelf space of retail stores significantly impacts the revenue of the retailer. Since customers typically tend to buy sets of items (i.e., itemsets) together, several research efforts have been undertaken towards facilitating itemset placement in retail stores for improving retailer revenue. However, they fail to consider that the time-period of expiry can vary across items i.e., some items expire sooner than others. This leads to loss of opportunity towards improving retailer revenue. Hence, we propose PEAR, which is a Product Expiry-Aware and Revenue-conscious itemset placement scheme for improving retailer revenue. Our key contributions are three-fold. First, we introduce the problem of addressing retail itemset placement when the items can be associated with different time-periods of expiry. Second, we propose the expiry-aware PEAR scheme for efficiently identifying and placing high-revenue itemsets for improving retailer revenue. Third, we conduct a performance study with two real datasets to demonstrate that PEAR is indeed effective in improving retailer revenue w.r.t. a reference scheme.","PeriodicalId":129612,"journal":{"name":"2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134295906","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

Classification of Space Particle Events using Supervised Machine Learning Algorithms 使用监督机器学习算法的空间粒子事件分类

2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2021-10-06 DOI: 10.1109/DSAA53316.2021.9564114

Rijad Sarić, Junchao Chen, M. Krstic, Edhem Čustović, G. Panic, Jasmin Kevric, D. Jokić

引用次数: 1

Atlastic Reputation AI: Four Years of Advancing and Applying a SOTA NLP Classifier 大西洋声誉人工智能:SOTA NLP分类器的四年发展和应用

2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2021-10-06 DOI: 10.1109/DSAA53316.2021.9564190

Viet Yen Nguyen, Andréa B. Duque, Jules Belveze, Lukas D. Baker, Astrid Harsaae, Pedro Pessoa, Inês Oliveira

引用次数: 0