Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498242
Erman Pattuk, Murat Kantarcioglu, Huseyin Ulusoy, B. Malin
Recent advances in personalized medicine point towards a future where clinical decision making will be dependent upon the individual characteristics of the patient, e.g., their age, race, genomic variation, and lifestyle. Already, there are numerous commercial entities working towards the provision of software to support such decisions as cloud-based services. However, deployment of such services in such settings raises important challenges for privacy. A recent attack shows that disclosing personalized drug dosage recommendations, combined with several pieces of demographic knowledge, can be leveraged to infer single nucleotide polymorphism variants of a patient. One manner to prevent such inference is to apply secure multi-party computation (SMC) techniques that hide all patient data, so that no information, including the clinical recommendation, is disclosed during the decision making process. Yet, SMC is a computationally cumbersome process and disclosing some information may be necessary for various compliance purposes. Additionally, certain information (e.g., demographic information) may already be publicly available. In this work, we provide a novel approach to selectively disclose certain information before the SMC process to significantly improve personalized decision making performance while preserving desired levels of privacy. To achieve this goal, we introduce mechanisms to quickly compute the loss in privacy due to information disclosure while considering its performance impact on SMC execution phase. Our empirical analysis show that we can achieve up to three orders of magnitude improvement compared to pure SMC solutions with only a slight increase in privacy risks.
{"title":"Optimizing secure classification performance with privacy-aware feature selection","authors":"Erman Pattuk, Murat Kantarcioglu, Huseyin Ulusoy, B. Malin","doi":"10.1109/ICDE.2016.7498242","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498242","url":null,"abstract":"Recent advances in personalized medicine point towards a future where clinical decision making will be dependent upon the individual characteristics of the patient, e.g., their age, race, genomic variation, and lifestyle. Already, there are numerous commercial entities working towards the provision of software to support such decisions as cloud-based services. However, deployment of such services in such settings raises important challenges for privacy. A recent attack shows that disclosing personalized drug dosage recommendations, combined with several pieces of demographic knowledge, can be leveraged to infer single nucleotide polymorphism variants of a patient. One manner to prevent such inference is to apply secure multi-party computation (SMC) techniques that hide all patient data, so that no information, including the clinical recommendation, is disclosed during the decision making process. Yet, SMC is a computationally cumbersome process and disclosing some information may be necessary for various compliance purposes. Additionally, certain information (e.g., demographic information) may already be publicly available. In this work, we provide a novel approach to selectively disclose certain information before the SMC process to significantly improve personalized decision making performance while preserving desired levels of privacy. To achieve this goal, we introduce mechanisms to quickly compute the loss in privacy due to information disclosure while considering its performance impact on SMC execution phase. Our empirical analysis show that we can achieve up to three orders of magnitude improvement compared to pure SMC solutions with only a slight increase in privacy risks.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"19 1","pages":"217-228"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76763927","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}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498264
Ahsanul Haque, L. Khan, M. Baron, B. Thuraisingham, C. Aggarwal
To decide if an update to a data stream classifier is necessary, existing sliding window based techniques monitor classifier performance on recent instances. If there is a significant change in classifier performance, these approaches determine a chunk boundary, and update the classifier. However, monitoring classifier performance is costly due to scarcity of labeled data. In our previous work, we presented a semi-supervised framework SAND, which uses change detection on classifier confidence to detect a concept drift. Unlike most approaches, it requires only a limited amount of labeled data to detect chunk boundaries and to update the classifier. However, SAND is expensive in terms of execution time due to exhaustive invocation of the change detection module. In this paper, we present an efficient framework, which is based on the same principle as SAND, but exploits dynamic programming and executes the change detection module selectively. Moreover, we provide theoretical justification of the confidence calculation, and show effect of a concept drift on subsequent confidence scores. Experiment results show efficiency of the proposed framework in terms of both accuracy and execution time.
{"title":"Efficient handling of concept drift and concept evolution over Stream Data","authors":"Ahsanul Haque, L. Khan, M. Baron, B. Thuraisingham, C. Aggarwal","doi":"10.1109/ICDE.2016.7498264","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498264","url":null,"abstract":"To decide if an update to a data stream classifier is necessary, existing sliding window based techniques monitor classifier performance on recent instances. If there is a significant change in classifier performance, these approaches determine a chunk boundary, and update the classifier. However, monitoring classifier performance is costly due to scarcity of labeled data. In our previous work, we presented a semi-supervised framework SAND, which uses change detection on classifier confidence to detect a concept drift. Unlike most approaches, it requires only a limited amount of labeled data to detect chunk boundaries and to update the classifier. However, SAND is expensive in terms of execution time due to exhaustive invocation of the change detection module. In this paper, we present an efficient framework, which is based on the same principle as SAND, but exploits dynamic programming and executes the change detection module selectively. Moreover, we provide theoretical justification of the confidence calculation, and show effect of a concept drift on subsequent confidence scores. Experiment results show efficiency of the proposed framework in terms of both accuracy and execution time.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"38 1","pages":"481-492"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78117274","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}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498286
Jinkyu Kim, Heonseok Ha, Byung-Gon Chun, Sungroh Yoon, S. Cha
As a great deal of data has been accumulated in various disciplines, the need for the integrative analysis of separate but relevant data sources is becoming more important. Combining data sources can provide global insight that is otherwise difficult to obtain from individual sources. Because of privacy, regulations, and other issues, many large-scale data repositories remain closed off from the outside, raising what has been termed the data silo issue. The huge volume of today's big data often leads to computational challenges, adding another layer of complexity to the solution. In this paper, we propose a novel method called collaborative analytics by ensemble learning (CABEL), which attempts to resolve the main hurdles regarding the silo issue: accuracy, privacy, and computational efficiency. CABEL represents the data stored in each silo as a compact aggregate of samples called the silo signature. The compact representation provides computational efficiency and privacy preservation but makes it challenging to produce accurate analytics. To resolve this challenge, we formulate the problem of attribute domain sampling and reconstruction, and propose a solution called the Chebyshev subset. To model collaborative efforts to analyze semantically linked but structurally disconnected databases, CABEL utilizes a new ensemble learning technique termed the weighted bagging of base classifiers. We demonstrate the effectiveness of CABEL by testing with a nationwide health-insurance data set containing approximately 4,182,000,000 records collected from the entire population of an Organisation for Economic Co-operation and Development (OECD) country in 2012. In our binary classification tests, CABEL achieved median recall, precision, and F-measure values of 89%, 64%, and 76%, respectively, although only 0.001-0.00001% of the original data was used for model construction, while maintaining data privacy and computational efficiency.
{"title":"Collaborative analytics for data silos","authors":"Jinkyu Kim, Heonseok Ha, Byung-Gon Chun, Sungroh Yoon, S. Cha","doi":"10.1109/ICDE.2016.7498286","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498286","url":null,"abstract":"As a great deal of data has been accumulated in various disciplines, the need for the integrative analysis of separate but relevant data sources is becoming more important. Combining data sources can provide global insight that is otherwise difficult to obtain from individual sources. Because of privacy, regulations, and other issues, many large-scale data repositories remain closed off from the outside, raising what has been termed the data silo issue. The huge volume of today's big data often leads to computational challenges, adding another layer of complexity to the solution. In this paper, we propose a novel method called collaborative analytics by ensemble learning (CABEL), which attempts to resolve the main hurdles regarding the silo issue: accuracy, privacy, and computational efficiency. CABEL represents the data stored in each silo as a compact aggregate of samples called the silo signature. The compact representation provides computational efficiency and privacy preservation but makes it challenging to produce accurate analytics. To resolve this challenge, we formulate the problem of attribute domain sampling and reconstruction, and propose a solution called the Chebyshev subset. To model collaborative efforts to analyze semantically linked but structurally disconnected databases, CABEL utilizes a new ensemble learning technique termed the weighted bagging of base classifiers. We demonstrate the effectiveness of CABEL by testing with a nationwide health-insurance data set containing approximately 4,182,000,000 records collected from the entire population of an Organisation for Economic Co-operation and Development (OECD) country in 2012. In our binary classification tests, CABEL achieved median recall, precision, and F-measure values of 89%, 64%, and 76%, respectively, although only 0.001-0.00001% of the original data was used for model construction, while maintaining data privacy and computational efficiency.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"152 1","pages":"743-754"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74904219","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}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498404
Olga Gkountouna, Manolis Terrovitis
Collections of real-world data usually have implicit or explicit structural relations. For example, databases link records through foreign keys, and XML documents express associations between different values through syntax. Privacy preservation, until now, has focused either on data with a very simple structure, e.g. relational tables, or on data with very complex structure e.g. social network graphs, but has ignored intermediate cases, which are the most frequent in practice. In this work, we focus on tree structured data. The paper defines k(m;n)-anonymity, which provides protection against identity disclosure and proposes a greedy anonymization heuristic that is able to sanitize large datasets. The algorithm and the quality of the anonymization are evaluated experimentally.
{"title":"Anonymizing collections of tree-structured data","authors":"Olga Gkountouna, Manolis Terrovitis","doi":"10.1109/ICDE.2016.7498404","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498404","url":null,"abstract":"Collections of real-world data usually have implicit or explicit structural relations. For example, databases link records through foreign keys, and XML documents express associations between different values through syntax. Privacy preservation, until now, has focused either on data with a very simple structure, e.g. relational tables, or on data with very complex structure e.g. social network graphs, but has ignored intermediate cases, which are the most frequent in practice. In this work, we focus on tree structured data. The paper defines k(m;n)-anonymity, which provides protection against identity disclosure and proposes a greedy anonymization heuristic that is able to sanitize large datasets. The algorithm and the quality of the anonymization are evaluated experimentally.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"52 1","pages":"1520-1521"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84418234","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}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498319
Ziawasch Abedjan, J. Morcos, I. Ilyas, M. Ouzzani, Paolo Papotti, M. Stonebraker
In data integration, data curation, and other data analysis tasks, users spend a considerable amount of time converting data from one representation to another. For example US dates to European dates or airport codes to city names. In a previous vision paper, we presented the initial design of DataXFormer, a system that uses web resources to assist in transformation discovery. Specifically, DataXFormer discovers possible transformations from web tables and web forms and involves human feedback where appropriate. In this paper, we present the full fledged system along with several extensions. In particular, we present algorithms to find (i) transformations that entail multiple columns of input data, (ii) indirect transformations that are compositions of other transformations, (iii) transformations that are not functions but rather relationships, and (iv) transformations from a knowledge base of public data. We report on experiments with a collection of 120 transformation tasks, and show our enhanced system automatically covers 101 of them by using openly available resources.
{"title":"DataXFormer: A robust transformation discovery system","authors":"Ziawasch Abedjan, J. Morcos, I. Ilyas, M. Ouzzani, Paolo Papotti, M. Stonebraker","doi":"10.1109/ICDE.2016.7498319","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498319","url":null,"abstract":"In data integration, data curation, and other data analysis tasks, users spend a considerable amount of time converting data from one representation to another. For example US dates to European dates or airport codes to city names. In a previous vision paper, we presented the initial design of DataXFormer, a system that uses web resources to assist in transformation discovery. Specifically, DataXFormer discovers possible transformations from web tables and web forms and involves human feedback where appropriate. In this paper, we present the full fledged system along with several extensions. In particular, we present algorithms to find (i) transformations that entail multiple columns of input data, (ii) indirect transformations that are compositions of other transformations, (iii) transformations that are not functions but rather relationships, and (iv) transformations from a knowledge base of public data. We report on experiments with a collection of 120 transformation tasks, and show our enhanced system automatically covers 101 of them by using openly available resources.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"99 1","pages":"1134-1145"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80568307","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}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498250
Aleksandar Vitorovic, Mohammed Elseidy, Christoph E. Koch
We address the problem of load balancing for parallel joins.We show that the distribution of input data received and the output data produced by worker machines are both important for performance. As a result, previous work, which optimizes either for input or output, stands ineffective for load balancing. To that end, we propose a multi-stage load-balancing algorithm which considers the properties of both input and output data through sampling of the original join matrix. To do this efficiently, we propose a novel category of equi-weight histograms. To build them, we exploit state-of-the-art computational geometry algorithms for rectangle tiling. To our knowledge, we are the first to employ tiling algorithms for join load-balancing. In addition, we propose a novel, join-specialized tiling algorithm that has drastically lower time and space complexity than existing algorithms. Experiments show that our scheme outperforms state-of-the-art techniques by up to a factor of 15.
{"title":"Load balancing and skew resilience for parallel joins","authors":"Aleksandar Vitorovic, Mohammed Elseidy, Christoph E. Koch","doi":"10.1109/ICDE.2016.7498250","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498250","url":null,"abstract":"We address the problem of load balancing for parallel joins.We show that the distribution of input data received and the output data produced by worker machines are both important for performance. As a result, previous work, which optimizes either for input or output, stands ineffective for load balancing. To that end, we propose a multi-stage load-balancing algorithm which considers the properties of both input and output data through sampling of the original join matrix. To do this efficiently, we propose a novel category of equi-weight histograms. To build them, we exploit state-of-the-art computational geometry algorithms for rectangle tiling. To our knowledge, we are the first to employ tiling algorithms for join load-balancing. In addition, we propose a novel, join-specialized tiling algorithm that has drastically lower time and space complexity than existing algorithms. Experiments show that our scheme outperforms state-of-the-art techniques by up to a factor of 15.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"1 1","pages":"313-324"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90870662","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}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498324
Wolf Rödiger, S. Idicula, A. Kemper, Thomas Neumann
Modern InfiniBand interconnects offer link speeds of several gigabytes per second and a remote direct memory access (RDMA) paradigm for zero-copy network communication. Both are crucial for parallel database systems to achieve scalable distributed query processing where adding a server to the cluster increases performance. However, the scalability of distributed joins is threatened by unexpected data characteristics: Skew can cause a severe load imbalance such that a single server has to process a much larger part of the input than its fair share and by this slows down the entire distributed query. We introduce Flow-Join, a novel distributed join algorithm that handles attribute value skew with minimal overhead. Flow-Join detects heavy hitters at runtime using small approximate histograms and adapts the redistribution scheme to resolve load imbalances before they impact the join performance. Previous approaches often involve expensive analysis phases, which slow down distributed join processing for non-skewed workloads. This is especially the case for modern high-speed interconnects, which are too fast to hide the extra computation. Other skew handling approaches require detailed statistics, which are often not available or overly inaccurate for intermediate results. In contrast, Flow-Join uses our novel lightweight skew handling scheme to execute at the full network speed of more than 6 GB/s for InfiniBand 4×FDR, joining a skewed input at 11.5 billion tuples/s with 32 servers. This is 6.8× faster than a standard distributed hash join using the same hardware. At the same time, Flow-Join does not compromise the join performance for non-skewed workloads.
{"title":"Flow-Join: Adaptive skew handling for distributed joins over high-speed networks","authors":"Wolf Rödiger, S. Idicula, A. Kemper, Thomas Neumann","doi":"10.1109/ICDE.2016.7498324","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498324","url":null,"abstract":"Modern InfiniBand interconnects offer link speeds of several gigabytes per second and a remote direct memory access (RDMA) paradigm for zero-copy network communication. Both are crucial for parallel database systems to achieve scalable distributed query processing where adding a server to the cluster increases performance. However, the scalability of distributed joins is threatened by unexpected data characteristics: Skew can cause a severe load imbalance such that a single server has to process a much larger part of the input than its fair share and by this slows down the entire distributed query. We introduce Flow-Join, a novel distributed join algorithm that handles attribute value skew with minimal overhead. Flow-Join detects heavy hitters at runtime using small approximate histograms and adapts the redistribution scheme to resolve load imbalances before they impact the join performance. Previous approaches often involve expensive analysis phases, which slow down distributed join processing for non-skewed workloads. This is especially the case for modern high-speed interconnects, which are too fast to hide the extra computation. Other skew handling approaches require detailed statistics, which are often not available or overly inaccurate for intermediate results. In contrast, Flow-Join uses our novel lightweight skew handling scheme to execute at the full network speed of more than 6 GB/s for InfiniBand 4×FDR, joining a skewed input at 11.5 billion tuples/s with 32 servers. This is 6.8× faster than a standard distributed hash join using the same hardware. At the same time, Flow-Join does not compromise the join performance for non-skewed workloads.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"14 1","pages":"1194-1205"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89689275","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}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498303
Hongzhi Yin, Zhiting Hu, Xiaofang Zhou, Hao Wang, Kai Zheng, Nguyen Quoc Viet Hung, S. Sadiq
Social community detection is a growing field of interest in the area of social network applications, and many approaches have been developed, including graph partitioning, latent space model, block model and spectral clustering. Most existing work purely focuses on network structure information which is, however, often sparse, noisy and lack of interpretability. To improve the accuracy and interpretability of community discovery, we propose to infer users' social communities by incorporating their spatiotemporal data and semantic information. Technically, we propose a unified probabilistic generative model, User-Community-Geo-Topic (UCGT), to simulate the generative process of communities as a result of network proximities, spatiotemporal co-occurrences and semantic similarity. With a well-designed multi-component model structure and a parallel inference implementation to leverage the power of multicores and clusters, our UCGT model is expressive while remaining efficient and scalable to growing large-scale geo-social networking data. We deploy UCGT to two application scenarios of user behavior predictions: check-in prediction and social interaction prediction. Extensive experiments on two large-scale geo-social networking datasets show that UCGT achieves better performance than existing state-of-the-art comparison methods.
{"title":"Discovering interpretable geo-social communities for user behavior prediction","authors":"Hongzhi Yin, Zhiting Hu, Xiaofang Zhou, Hao Wang, Kai Zheng, Nguyen Quoc Viet Hung, S. Sadiq","doi":"10.1109/ICDE.2016.7498303","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498303","url":null,"abstract":"Social community detection is a growing field of interest in the area of social network applications, and many approaches have been developed, including graph partitioning, latent space model, block model and spectral clustering. Most existing work purely focuses on network structure information which is, however, often sparse, noisy and lack of interpretability. To improve the accuracy and interpretability of community discovery, we propose to infer users' social communities by incorporating their spatiotemporal data and semantic information. Technically, we propose a unified probabilistic generative model, User-Community-Geo-Topic (UCGT), to simulate the generative process of communities as a result of network proximities, spatiotemporal co-occurrences and semantic similarity. With a well-designed multi-component model structure and a parallel inference implementation to leverage the power of multicores and clusters, our UCGT model is expressive while remaining efficient and scalable to growing large-scale geo-social networking data. We deploy UCGT to two application scenarios of user behavior predictions: check-in prediction and social interaction prediction. Extensive experiments on two large-scale geo-social networking datasets show that UCGT achieves better performance than existing state-of-the-art comparison methods.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"2 1","pages":"942-953"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91534110","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}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498249
Duy-Hung Phan, P. Michiardi
Data summarization is essential for users to interact with data. Current state of the art algorithms to optimize its most general form, the multiple Group By queries, have limitations in scalability. In this paper, we propose a novel algorithm, Top-Down Splitting, that scales to hundreds or even thousands of attributes and queries, and that quickly and efficiently produces optimized query execution plans. We analyze the complexity of our algorithm, and evaluate, empirically, its scalability and effectiveness through an experimental campaign. Results show that our algorithm is remarkably faster than alternatives in prior works, while generally producing better solutions. Ultimately, our algorithm reduces up to 34% the query execution time, when compared to un-optimized plans.
{"title":"A novel, low-latency algorithm for multiple Group-By query optimization","authors":"Duy-Hung Phan, P. Michiardi","doi":"10.1109/ICDE.2016.7498249","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498249","url":null,"abstract":"Data summarization is essential for users to interact with data. Current state of the art algorithms to optimize its most general form, the multiple Group By queries, have limitations in scalability. In this paper, we propose a novel algorithm, Top-Down Splitting, that scales to hundreds or even thousands of attributes and queries, and that quickly and efficiently produces optimized query execution plans. We analyze the complexity of our algorithm, and evaluate, empirically, its scalability and effectiveness through an experimental campaign. Results show that our algorithm is remarkably faster than alternatives in prior works, while generally producing better solutions. Ultimately, our algorithm reduces up to 34% the query execution time, when compared to un-optimized plans.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"25 1","pages":"301-312"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87882363","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}
Pub Date : 2016-05-16DOI: 10.1109/ICDE.2016.7498349
E. Rezig, Eduard Constantin Dragut, M. Ouzzani, A. Elmagarmid, Walid G. Aref
With the exponential growth of data on the Web comes the opportunity to integrate multiple sources to give more accurate answers to user queries. Upon retrieving records from multiple Web databases, a key task is to merge records that refer to the same real-world entity. We demonstrate ORLF (Online Record Linkage and Fusion), a flexible query-time record linkage and fusion framework. ORLF deduplicates newly arriving query results jointly with previously processed query results. We use an iterative caching solution that leverages query locality to effectively deduplicate newly incoming records with cached records. ORLF aims to deliver timely query answers that are duplicate-free and reflect knowledge collected from previous queries.
随着Web上数据的指数级增长，有机会集成多个来源，从而为用户查询提供更准确的答案。在从多个Web数据库检索记录时，一个关键任务是合并引用同一真实实体的记录。我们演示了一种灵活的查询时间记录链接和融合框架ORLF (Online Record Linkage and Fusion)。ORLF将新到达的查询结果与先前处理过的查询结果一起去重。我们使用迭代缓存解决方案，利用查询局域性有效地用缓存记录去重复新传入的记录。ORLF旨在提供及时的、无重复的查询答案，并反映从以前的查询中收集的知识。
{"title":"ORLF: A flexible framework for online record linkage and fusion","authors":"E. Rezig, Eduard Constantin Dragut, M. Ouzzani, A. Elmagarmid, Walid G. Aref","doi":"10.1109/ICDE.2016.7498349","DOIUrl":"https://doi.org/10.1109/ICDE.2016.7498349","url":null,"abstract":"With the exponential growth of data on the Web comes the opportunity to integrate multiple sources to give more accurate answers to user queries. Upon retrieving records from multiple Web databases, a key task is to merge records that refer to the same real-world entity. We demonstrate ORLF (Online Record Linkage and Fusion), a flexible query-time record linkage and fusion framework. ORLF deduplicates newly arriving query results jointly with previously processed query results. We use an iterative caching solution that leverages query locality to effectively deduplicate newly incoming records with cached records. ORLF aims to deliver timely query answers that are duplicate-free and reflect knowledge collected from previous queries.","PeriodicalId":6883,"journal":{"name":"2016 IEEE 32nd International Conference on Data Engineering (ICDE)","volume":"1 1","pages":"1378-1381"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87982704","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}
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