Alexander van Renen, Viktor Leis, A. Kemper, Thomas Neumann, T. Hashida, Kazuichi Oe, Y. Doi, L. Harada, Mitsuru Sato
Non-volatile memory (NVM) is a new storage technology that combines the performance and byte addressability of DRAM with the persistence of traditional storage devices like flash (SSD). While these properties make NVM highly promising, it is not yet clear how to best integrate NVM into the storage layer of modern database systems. Two system designs have been proposed. The first is to use NVM exclusively, i.e., to store all data and index structures on it. However, because NVM has a higher latency than DRAM, this design can be less efficient than main-memory database systems. For this reason, the second approach uses a page-based DRAM cache in front of NVM. This approach, however, does not utilize the byte addressability of NVM and, as a result, accessing an uncached tuple on NVM requires retrieving an entire page. In this work, we evaluate these two approaches and compare them with in-memory databases as well as more traditional buffer managers that use main memory as a cache in front of SSDs. This allows us to determine how much performance gain can be expected from NVM. We also propose a lightweight storage manager that simultaneously supports DRAM, NVM, and flash. Our design utilizes the byte addressability of NVM and uses it as an additional caching layer that improves performance without losing the benefits from the even faster DRAM and the large capacities of SSDs.
{"title":"Managing Non-Volatile Memory in Database Systems","authors":"Alexander van Renen, Viktor Leis, A. Kemper, Thomas Neumann, T. Hashida, Kazuichi Oe, Y. Doi, L. Harada, Mitsuru Sato","doi":"10.1145/3183713.3196897","DOIUrl":"https://doi.org/10.1145/3183713.3196897","url":null,"abstract":"Non-volatile memory (NVM) is a new storage technology that combines the performance and byte addressability of DRAM with the persistence of traditional storage devices like flash (SSD). While these properties make NVM highly promising, it is not yet clear how to best integrate NVM into the storage layer of modern database systems. Two system designs have been proposed. The first is to use NVM exclusively, i.e., to store all data and index structures on it. However, because NVM has a higher latency than DRAM, this design can be less efficient than main-memory database systems. For this reason, the second approach uses a page-based DRAM cache in front of NVM. This approach, however, does not utilize the byte addressability of NVM and, as a result, accessing an uncached tuple on NVM requires retrieving an entire page. In this work, we evaluate these two approaches and compare them with in-memory databases as well as more traditional buffer managers that use main memory as a cache in front of SSDs. This allows us to determine how much performance gain can be expected from NVM. We also propose a lightweight storage manager that simultaneously supports DRAM, NVM, and flash. Our design utilizes the byte addressability of NVM and uses it as an additional caching layer that improves performance without losing the benefits from the even faster DRAM and the large capacities of SSDs.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88098087","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}
Jinfeng Li, Xiao Yan, Jian Zhang, An Xu, James Cheng, Jie Liu, K. K. Ng, Ti-Chung Cheng
As an effective solution to the approximate nearest neighbors (ANN) search problem, learning to hash (L2H) is able to learn similarity-preserving hash functions tailored for a given dataset. However, existing L2H research mainly focuses on improving query performance by learning good hash functions, while Hamming ranking (HR) is used as the default querying method. We show by analysis and experiments that Hamming distance, the similarity indicator used in HR, is too coarse-grained and thus limits the performance of query processing. We propose a new fine-grained similarity indicator, quantization distance (QD), which provides more information about the similarity between a query and the items in a bucket. We then develop two efficient querying methods based on QD, which achieve significantly better query performance than HR. Our methods are general and can work with various L2H algorithms. Our experiments demonstrate that a simple and elegant querying method can produce performance gain equivalent to advanced and complicated learning algorithms.
{"title":"A General and Efficient Querying Method for Learning to Hash","authors":"Jinfeng Li, Xiao Yan, Jian Zhang, An Xu, James Cheng, Jie Liu, K. K. Ng, Ti-Chung Cheng","doi":"10.1145/3183713.3183750","DOIUrl":"https://doi.org/10.1145/3183713.3183750","url":null,"abstract":"As an effective solution to the approximate nearest neighbors (ANN) search problem, learning to hash (L2H) is able to learn similarity-preserving hash functions tailored for a given dataset. However, existing L2H research mainly focuses on improving query performance by learning good hash functions, while Hamming ranking (HR) is used as the default querying method. We show by analysis and experiments that Hamming distance, the similarity indicator used in HR, is too coarse-grained and thus limits the performance of query processing. We propose a new fine-grained similarity indicator, quantization distance (QD), which provides more information about the similarity between a query and the items in a bucket. We then develop two efficient querying methods based on QD, which achieve significantly better query performance than HR. Our methods are general and can work with various L2H algorithms. Our experiments demonstrate that a simple and elegant querying method can produce performance gain equivalent to advanced and complicated learning algorithms.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88236276","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}
Stratos Idreos, Konstantinos Zoumpatianos, Brian Hentschel, Michael S. Kester, Demi Guo
Data structures are critical in any data-driven scenario, but they are notoriously hard to design due to a massive design space and the dependence of performance on workload and hardware which evolve continuously. We present a design engine, the Data Calculator, which enables interactive and semi-automated design of data structures. It brings two innovations. First, it offers a set of fine-grained design primitives that capture the first principles of data layout design: how data structure nodes lay data out, and how they are positioned relative to each other. This allows for a structured description of the universe of possible data structure designs that can be synthesized as combinations of those primitives. The second innovation is computation of performance using learned cost models. These models are trained on diverse hardware and data profiles and capture the cost properties of fundamental data access primitives (e.g., random access). With these models, we synthesize the performance cost of complex operations on arbitrary data structure designs without having to: 1) implement the data structure, 2) run the workload, or even 3) access the target hardware. We demonstrate that the Data Calculator can assist data structure designers and researchers by accurately answering rich what-if design questions on the order of a few seconds or minutes, i.e., computing how the performance (response time) of a given data structure design is impacted by variations in the: 1) design, 2) hardware, 3) data, and 4) query workloads. This makes it effortless to test numerous designs and ideas before embarking on lengthy implementation, deployment, and hardware acquisition steps. We also demonstrate that the Data Calculator can synthesize entirely new designs, auto-complete partial designs, and detect suboptimal design choices.
{"title":"The Data Calculator: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models","authors":"Stratos Idreos, Konstantinos Zoumpatianos, Brian Hentschel, Michael S. Kester, Demi Guo","doi":"10.1145/3183713.3199671","DOIUrl":"https://doi.org/10.1145/3183713.3199671","url":null,"abstract":"Data structures are critical in any data-driven scenario, but they are notoriously hard to design due to a massive design space and the dependence of performance on workload and hardware which evolve continuously. We present a design engine, the Data Calculator, which enables interactive and semi-automated design of data structures. It brings two innovations. First, it offers a set of fine-grained design primitives that capture the first principles of data layout design: how data structure nodes lay data out, and how they are positioned relative to each other. This allows for a structured description of the universe of possible data structure designs that can be synthesized as combinations of those primitives. The second innovation is computation of performance using learned cost models. These models are trained on diverse hardware and data profiles and capture the cost properties of fundamental data access primitives (e.g., random access). With these models, we synthesize the performance cost of complex operations on arbitrary data structure designs without having to: 1) implement the data structure, 2) run the workload, or even 3) access the target hardware. We demonstrate that the Data Calculator can assist data structure designers and researchers by accurately answering rich what-if design questions on the order of a few seconds or minutes, i.e., computing how the performance (response time) of a given data structure design is impacted by variations in the: 1) design, 2) hardware, 3) data, and 4) query workloads. This makes it effortless to test numerous designs and ideas before embarking on lengthy implementation, deployment, and hardware acquisition steps. We also demonstrate that the Data Calculator can synthesize entirely new designs, auto-complete partial designs, and detect suboptimal design choices.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88357907","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}
Ben McCamish, Vahid Ghadakchi, Arash Termehchy, B. Touri, Liang Huang
As many users do not precisely know the structure and/or the content of databases, their queries do not exactly reflect their information needs. The database management systems (DBMS) may interact with users and leverage their feedback on the returned results to learn the information needs behind users' queries. Current query interfaces assume that users follow a fixed strategy of expressing their information needs, that is, the likelihood by which a user submits a query to express an information need remains unchanged during her interaction with the DBMS. Using a real-world interaction workload, we show that users learn and modify how to express their information needs during their interactions with the DBMS. We also show that users' learning is accurately modeled by a well-known reinforcement learning mechanism. As current data interaction systems assume that users do not modify their strategies, they cannot discover the information needs behind users' queries effectively. We model the interaction between users and DBMS as a game with identical interest between two rational agents whose goal is to establish a common language for representing information needs in form of queries. We propose a reinforcement learning method that learns and answers the information needs behind queries and adapts to the changes in users' strategies and prove that it improves the effectiveness of answering queries stochastically speaking. We analyze the challenges of efficient implementation of this method over large-scale relational databases and propose two efficient adaptations of this algorithm over large-scale relational databases. Our extensive empirical studies over real-world query workloads and large-scale relational databases indicate that our algorithms are efficient. Our empirical results also show that our proposed learning mechanism is more effective than the state-of-the-art query answering method.
{"title":"The Data Interaction Game","authors":"Ben McCamish, Vahid Ghadakchi, Arash Termehchy, B. Touri, Liang Huang","doi":"10.1145/3183713.3196899","DOIUrl":"https://doi.org/10.1145/3183713.3196899","url":null,"abstract":"As many users do not precisely know the structure and/or the content of databases, their queries do not exactly reflect their information needs. The database management systems (DBMS) may interact with users and leverage their feedback on the returned results to learn the information needs behind users' queries. Current query interfaces assume that users follow a fixed strategy of expressing their information needs, that is, the likelihood by which a user submits a query to express an information need remains unchanged during her interaction with the DBMS. Using a real-world interaction workload, we show that users learn and modify how to express their information needs during their interactions with the DBMS. We also show that users' learning is accurately modeled by a well-known reinforcement learning mechanism. As current data interaction systems assume that users do not modify their strategies, they cannot discover the information needs behind users' queries effectively. We model the interaction between users and DBMS as a game with identical interest between two rational agents whose goal is to establish a common language for representing information needs in form of queries. We propose a reinforcement learning method that learns and answers the information needs behind queries and adapts to the changes in users' strategies and prove that it improves the effectiveness of answering queries stochastically speaking. We analyze the challenges of efficient implementation of this method over large-scale relational databases and propose two efficient adaptations of this algorithm over large-scale relational databases. Our extensive empirical studies over real-world query workloads and large-scale relational databases indicate that our algorithms are efficient. Our empirical results also show that our proposed learning mechanism is more effective than the state-of-the-art query answering method.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86726385","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}
{"title":"Session details: Research 15: Databases for Emerging Hardware","authors":"P. Pietzuch","doi":"10.1145/3258023","DOIUrl":"https://doi.org/10.1145/3258023","url":null,"abstract":"","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"57 6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85435036","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}
Trajectory analytics can benefit many real-world applications, e.g., frequent trajectory based navigation systems, road planning, car pooling, and transportation optimizations. Existing algorithms focus on optimizing this problem in a single machine. However, the amount of trajectories exceeds the storage and processing capability of a single machine, and it calls for large-scale trajectory analytics in distributed environments. The distributed trajectory analytics faces challenges of data locality aware partitioning, load balance, easy-to-use interface, and versatility to support various trajectory similarity functions. To address these challenges, we propose a distributed in-memory trajectory analytics system DITA. We propose an effective partitioning method, global index and local index, to address the data locality problem. We devise cost-based techniques to balance the workload. We develop a filter-verification framework to improve the performance. Moreover, DITA can support most of existing similarity functions to quantify the similarity between trajectories. We integrate our framework seamlessly into Spark SQL, and make it support SQL and DataFrame API interfaces. We have conducted extensive experiments on real world datasets, and experimental results show that DITA outperforms existing distributed trajectory similarity search and join approaches significantly.
{"title":"DITA: Distributed In-Memory Trajectory Analytics","authors":"Zeyuan Shang, Guoliang Li, Z. Bao","doi":"10.1145/3183713.3183743","DOIUrl":"https://doi.org/10.1145/3183713.3183743","url":null,"abstract":"Trajectory analytics can benefit many real-world applications, e.g., frequent trajectory based navigation systems, road planning, car pooling, and transportation optimizations. Existing algorithms focus on optimizing this problem in a single machine. However, the amount of trajectories exceeds the storage and processing capability of a single machine, and it calls for large-scale trajectory analytics in distributed environments. The distributed trajectory analytics faces challenges of data locality aware partitioning, load balance, easy-to-use interface, and versatility to support various trajectory similarity functions. To address these challenges, we propose a distributed in-memory trajectory analytics system DITA. We propose an effective partitioning method, global index and local index, to address the data locality problem. We devise cost-based techniques to balance the workload. We develop a filter-verification framework to improve the performance. Moreover, DITA can support most of existing similarity functions to quantify the similarity between trajectories. We integrate our framework seamlessly into Spark SQL, and make it support SQL and DataFrame API interfaces. We have conducted extensive experiments on real world datasets, and experimental results show that DITA outperforms existing distributed trajectory similarity search and join approaches significantly.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84202353","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}
RDF Question/Answering(Q/A) systems can interpret user's question N as SPARQL query Q and return answer set $Q(D)$ over RDF repository D to the user. However, due to the complexity of linking natural phrases with specific RDF items (e.g., entities and predicates), it remains difficult to understand users' questions precisely, hence $Q(D)$ may not meet users' expectation, offering wrong answers and dismissing some correct answers. In this demo, we design an I Interactive Mechanism aiming for PRO motion V ia feedback to Q/A systems (IMPROVE-QA), a whole platform to make existing Q/A systems return more precise answers (denoted as $mathcal Q^prime (D)$) to users. Based on user's feedback over $Q(D)$, IMPROVE-QA automatically refines the original query Q into a new query graph $mathcal Q^prime $ with minimum modifications, where $mathcal Q^prime (D)$ provides more precise answers. We will also demonstrate how IMPROVE-QA can apply the "lesson'' learned from the user in each query to improve the precision of Q/A systems on subsequent natural language questions.
{"title":"IMPROVE-QA: An Interactive Mechanism for RDF Question/Answering Systems","authors":"Xinbo Zhang, Lei Zou","doi":"10.1145/3183713.3193555","DOIUrl":"https://doi.org/10.1145/3183713.3193555","url":null,"abstract":"RDF Question/Answering(Q/A) systems can interpret user's question N as SPARQL query Q and return answer set $Q(D)$ over RDF repository D to the user. However, due to the complexity of linking natural phrases with specific RDF items (e.g., entities and predicates), it remains difficult to understand users' questions precisely, hence $Q(D)$ may not meet users' expectation, offering wrong answers and dismissing some correct answers. In this demo, we design an I Interactive Mechanism aiming for PRO motion V ia feedback to Q/A systems (IMPROVE-QA), a whole platform to make existing Q/A systems return more precise answers (denoted as $mathcal Q^prime (D)$) to users. Based on user's feedback over $Q(D)$, IMPROVE-QA automatically refines the original query Q into a new query graph $mathcal Q^prime $ with minimum modifications, where $mathcal Q^prime (D)$ provides more precise answers. We will also demonstrate how IMPROVE-QA can apply the \"lesson'' learned from the user in each query to improve the precision of Q/A systems on subsequent natural language questions.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90717749","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}
Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, G. Miklau
The adoption of differential privacy is growing but the complexity of designing private, efficient and accurate algorithms is still high. We propose a novel programming framework and system, Ektelo, for implementing both existing and new privacy algorithms. For the task of answering linear counting queries, we show that nearly all existing algorithms can be composed from operators, each conforming to one of a small number of operator classes. While past programming frameworks have helped to ensure the privacy of programs, the novelty of our framework is its significant support for authoring accurate and efficient (as well as private) programs. After describing the design and architecture of the Ektelo system, we show that Ektelo is expressive, that it allows for safer implementations through code reuse, and that it allows both privacy novices and experts to easily design algorithms. We demonstrate the use of Ektelo by designing several new state-of-the-art algorithms.
{"title":"EKTELO: A Framework for Defining Differentially-Private Computations","authors":"Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, G. Miklau","doi":"10.1145/3183713.3196921","DOIUrl":"https://doi.org/10.1145/3183713.3196921","url":null,"abstract":"The adoption of differential privacy is growing but the complexity of designing private, efficient and accurate algorithms is still high. We propose a novel programming framework and system, Ektelo, for implementing both existing and new privacy algorithms. For the task of answering linear counting queries, we show that nearly all existing algorithms can be composed from operators, each conforming to one of a small number of operator classes. While past programming frameworks have helped to ensure the privacy of programs, the novelty of our framework is its significant support for authoring accurate and efficient (as well as private) programs. After describing the design and architecture of the Ektelo system, we show that Ektelo is expressive, that it allows for safer implementations through code reuse, and that it allows both privacy novices and experts to easily design algorithms. We demonstrate the use of Ektelo by designing several new state-of-the-art algorithms.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90054547","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}
Min Xie, R. C. Wong, J. Li, Cheng Long, Ashwin Lall
Extracting interesting tuples from a large database is an important problem in multi-criteria decision making. Two representative queries were proposed in the literature: top- k queries and skyline queries. A top- k query requires users to specify their utility functions beforehand and then returns k tuples to the users. A skyline query does not require any utility function from users but it puts no control on the number of tuples returned to users. Recently, a k-regret query was proposed and received attention from the community because it does not require any utility function from users and the output size is controllable, and thus it avoids those deficiencies of top- k queries and skyline queries. Specifically, it returns k tuples that minimize a criterion called the maximum regret ratio . In this paper, we present the lower bound of the maximum regret ratio for the k -regret query. Besides, we propose a novel algorithm, called SPHERE, whose upper bound on the maximum regret ratio is asymptotically optimal and restriction-free for any dimensionality, the best-known result in the literature. We conducted extensive experiments to show that SPHERE performs better than the state-of-the-art methods for the k -regret query.
{"title":"Efficient k-Regret Query Algorithm with Restriction-free Bound for any Dimensionality","authors":"Min Xie, R. C. Wong, J. Li, Cheng Long, Ashwin Lall","doi":"10.1145/3183713.3196903","DOIUrl":"https://doi.org/10.1145/3183713.3196903","url":null,"abstract":"Extracting interesting tuples from a large database is an important problem in multi-criteria decision making. Two representative queries were proposed in the literature: top- k queries and skyline queries. A top- k query requires users to specify their utility functions beforehand and then returns k tuples to the users. A skyline query does not require any utility function from users but it puts no control on the number of tuples returned to users. Recently, a k-regret query was proposed and received attention from the community because it does not require any utility function from users and the output size is controllable, and thus it avoids those deficiencies of top- k queries and skyline queries. Specifically, it returns k tuples that minimize a criterion called the maximum regret ratio . In this paper, we present the lower bound of the maximum regret ratio for the k -regret query. Besides, we propose a novel algorithm, called SPHERE, whose upper bound on the maximum regret ratio is asymptotically optimal and restriction-free for any dimensionality, the best-known result in the literature. We conducted extensive experiments to show that SPHERE performs better than the state-of-the-art methods for the k -regret query.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78730928","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}
Knowledge base construction (KBC) has become a hot and in-time topic recently with the increasing application need of large-scale knowledge bases (KBs), such as semantic search, QA systems, the Google Knowledge Graph and IBM Watson QA System. Existing KBs mainly focus on encoding the factual facts of the world, e.g., city area and company product, which are regarded as the objective knowledge, whereas the subjective knowledge, which is frequently mentioned in Web queries, has been neglected. The subjective knowledge has no documented ground truth, instead, the truth relies on people's dominant opinion, which can be solicited from online crowd workers. In our work, we propose a KBC framework for subjective knowledge base construction taking advantage of the knowledge from the crowd and existing KBs. We develop a two-staged framework for subjective KB construction which consists of core subjective KB construction and subjective KB enrichment. Firstly, we try to build a core subjective KB mined from existing KBs, where every instance has rich objective properties. Then, we populate the core subjective KB with instances extracted from existing KBs, in which the crowd is leverage to annotate the subjective property of the instances. In order to optimize the crowd annotation process, we formulate the problem of subjective KB enrichment procedure as a cost-aware instance annotation problem and propose two instance annotation algorithms, i.e., adaptive instance annotation and batch-mode instance annotation algorithms. We develop a two-stage system for subjective KB construction which consists of core subjective KB construction and subjective knowledge enrichment. We evaluate our framework on real knowledge bases and a real crowdsourcing platform, the experimental results show that we can derive high quality subjective knowledge facts from existing KBs and crowdsourcing techniques through our proposed framework.
{"title":"Subjective Knowledge Base Construction Powered By Crowdsourcing and Knowledge Base","authors":"Hao Xin, Rui Meng, Lei Chen","doi":"10.1145/3183713.3183732","DOIUrl":"https://doi.org/10.1145/3183713.3183732","url":null,"abstract":"Knowledge base construction (KBC) has become a hot and in-time topic recently with the increasing application need of large-scale knowledge bases (KBs), such as semantic search, QA systems, the Google Knowledge Graph and IBM Watson QA System. Existing KBs mainly focus on encoding the factual facts of the world, e.g., city area and company product, which are regarded as the objective knowledge, whereas the subjective knowledge, which is frequently mentioned in Web queries, has been neglected. The subjective knowledge has no documented ground truth, instead, the truth relies on people's dominant opinion, which can be solicited from online crowd workers. In our work, we propose a KBC framework for subjective knowledge base construction taking advantage of the knowledge from the crowd and existing KBs. We develop a two-staged framework for subjective KB construction which consists of core subjective KB construction and subjective KB enrichment. Firstly, we try to build a core subjective KB mined from existing KBs, where every instance has rich objective properties. Then, we populate the core subjective KB with instances extracted from existing KBs, in which the crowd is leverage to annotate the subjective property of the instances. In order to optimize the crowd annotation process, we formulate the problem of subjective KB enrichment procedure as a cost-aware instance annotation problem and propose two instance annotation algorithms, i.e., adaptive instance annotation and batch-mode instance annotation algorithms. We develop a two-stage system for subjective KB construction which consists of core subjective KB construction and subjective knowledge enrichment. We evaluate our framework on real knowledge bases and a real crowdsourcing platform, the experimental results show that we can derive high quality subjective knowledge facts from existing KBs and crowdsourcing techniques through our proposed framework.","PeriodicalId":20430,"journal":{"name":"Proceedings of the 2018 International Conference on Management of Data","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77969532","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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