This paper proposes a new data-placement method named adaptive overlapped declustering, which can be applied to a parallel storage system using a value range partitioning-based distributed directory and primary-backup data replication, to improve the space utilization by balancing their access loads. The proposed method reduces data skews generated by data migration for balancing access load. While some data-placement methods capable of balancing access load or reducing data skew have been proposed, both requirements satisfied simultaneously. The proposed method also improves the reliability and availability of the system because it reduces recovery time for damaged backups after a disk failure. The method achieves this acceleration by reducing a large amount of network communications and disk I/O. Mathematical analysis shows the efficiency of space utilization under skewed access workloads. Queuing simulations demonstrated that the proposed method halves backup restoration time, compared with the traditional chained declustering method.
{"title":"Adaptive overlapped declustering: a highly available data-placement method balancing access load and space utilization","authors":"Akitsugu Watanabe, H. Yokota","doi":"10.1109/ICDE.2005.16","DOIUrl":"https://doi.org/10.1109/ICDE.2005.16","url":null,"abstract":"This paper proposes a new data-placement method named adaptive overlapped declustering, which can be applied to a parallel storage system using a value range partitioning-based distributed directory and primary-backup data replication, to improve the space utilization by balancing their access loads. The proposed method reduces data skews generated by data migration for balancing access load. While some data-placement methods capable of balancing access load or reducing data skew have been proposed, both requirements satisfied simultaneously. The proposed method also improves the reliability and availability of the system because it reduces recovery time for damaged backups after a disk failure. The method achieves this acceleration by reducing a large amount of network communications and disk I/O. Mathematical analysis shows the efficiency of space utilization under skewed access workloads. Queuing simulations demonstrated that the proposed method halves backup restoration time, compared with the traditional chained declustering method.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"261 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115012446","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}
Schema matching is the problem of identifying corresponding elements in different schemas. Discovering these correspondences or matches is inherently difficult to automate. Past solutions have proposed a principled combination of multiple algorithms. However, these solutions sometimes perform rather poorly due to the lack of sufficient evidence in the schemas being matched. In this paper we show how a corpus of schemas and mappings can be used to augment the evidence about the schemas being matched, so they can be matched better. Such a corpus typically contains multiple schemas that model similar concepts and hence enables us to learn variations in the elements and their properties. We exploit such a corpus in two ways. First, we increase the evidence about each element being matched by including evidence from similar elements in the corpus. Second, we learn statistics about elements and their relationships and use them to infer constraints that we use to prune candidate mappings. We also describe how to use known mappings to learn the importance of domain and generic constraints. We present experimental results that demonstrate corpus-based matching outperforms direct matching (without the benefit of a corpus) in multiple domains.
{"title":"Corpus-based schema matching","authors":"J. Madhavan, P. Bernstein, A. Doan, A. Halevy","doi":"10.1109/ICDE.2005.39","DOIUrl":"https://doi.org/10.1109/ICDE.2005.39","url":null,"abstract":"Schema matching is the problem of identifying corresponding elements in different schemas. Discovering these correspondences or matches is inherently difficult to automate. Past solutions have proposed a principled combination of multiple algorithms. However, these solutions sometimes perform rather poorly due to the lack of sufficient evidence in the schemas being matched. In this paper we show how a corpus of schemas and mappings can be used to augment the evidence about the schemas being matched, so they can be matched better. Such a corpus typically contains multiple schemas that model similar concepts and hence enables us to learn variations in the elements and their properties. We exploit such a corpus in two ways. First, we increase the evidence about each element being matched by including evidence from similar elements in the corpus. Second, we learn statistics about elements and their relationships and use them to infer constraints that we use to prune candidate mappings. We also describe how to use known mappings to learn the importance of domain and generic constraints. We present experimental results that demonstrate corpus-based matching outperforms direct matching (without the benefit of a corpus) in multiple domains.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123330488","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}
Query personalization is the process of dynamically enhancing a query with related user preferences stored in a user profile with the aim of providing personalized answers. The underlying idea is that different users may find different things relevant to a search due to different preferences. Essential ingredients of query personalization are: (a) a model for representing and storing preferences in user profiles, and (b) algorithms for the generation of personalized answers using stored preferences. Modeling the plethora of preference types is a challenge. In this paper, we present a preference model that combines expressivity and concision. In addition, we provide efficient algorithms for the selection of preferences related to a query, and an algorithm for the progressive generation of personalized results, which are ranked based on user interest. Several classes of ranking functions are provided for this purpose. We present results of experiments both synthetic and with real users (a) demonstrating the efficiency of our algorithms, (b) showing the benefits of query personalization, and (c) providing insight as to the appropriateness of the proposed ranking functions.
{"title":"Personalized queries under a generalized preference model","authors":"G. Koutrika, Y. Ioannidis","doi":"10.1109/ICDE.2005.106","DOIUrl":"https://doi.org/10.1109/ICDE.2005.106","url":null,"abstract":"Query personalization is the process of dynamically enhancing a query with related user preferences stored in a user profile with the aim of providing personalized answers. The underlying idea is that different users may find different things relevant to a search due to different preferences. Essential ingredients of query personalization are: (a) a model for representing and storing preferences in user profiles, and (b) algorithms for the generation of personalized answers using stored preferences. Modeling the plethora of preference types is a challenge. In this paper, we present a preference model that combines expressivity and concision. In addition, we provide efficient algorithms for the selection of preferences related to a query, and an algorithm for the progressive generation of personalized results, which are ranked based on user interest. Several classes of ranking functions are provided for this purpose. We present results of experiments both synthetic and with real users (a) demonstrating the efficiency of our algorithms, (b) showing the benefits of query personalization, and (c) providing insight as to the appropriateness of the proposed ranking functions.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124599338","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}
Current approaches for estimating the cardinality of XML queries are applicable to a static scenario wherein the underlying XML data does not change subsequent to the collection of statistics on the repository. However, in practice, many XML-based applications are dynamic and involve frequent updates to the data. In this paper, we investigate efficient strategies for incrementally maintaining statistical summaries as and when updates are applied to the data. Specifically, we propose algorithms that handle both the addition of new documents as well as random insertions in the existing document trees. We also show, through a detailed performance evaluation, that our incremental techniques are significantly faster than the naive recomputation approach; and that estimation accuracy can be maintained even with a fixed memory budget.
{"title":"IMAX: incremental maintenance of schema-based XML statistics","authors":"Maya Ramanath, L. Zhang, J. Freire, J. Haritsa","doi":"10.1109/ICDE.2005.75","DOIUrl":"https://doi.org/10.1109/ICDE.2005.75","url":null,"abstract":"Current approaches for estimating the cardinality of XML queries are applicable to a static scenario wherein the underlying XML data does not change subsequent to the collection of statistics on the repository. However, in practice, many XML-based applications are dynamic and involve frequent updates to the data. In this paper, we investigate efficient strategies for incrementally maintaining statistical summaries as and when updates are applied to the data. Specifically, we propose algorithms that handle both the addition of new documents as well as random insertions in the existing document trees. We also show, through a detailed performance evaluation, that our incremental techniques are significantly faster than the naive recomputation approach; and that estimation accuracy can be maintained even with a fixed memory budget.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128888345","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}
The filtering of XML data is the basis of many complex applications. Lots of algorithms have been proposed to solve this problem. One important challenge is that the number of path queries is huge. It is necessary to take an efficient data structure representing path queries. Another challenge is that these path queries usually vary with time. The maintenance of path queries determines the flexibility and capacity of a filtering system. In this paper, we introduce a novel approximate method for XML data filtering, which uses Bloom filters representing path queries. In this method, millions of path queries can be stored efficiently At the same time, it is easy to deal with the change of these path queries. To improve the filtering performance, we introduce a new data structure, Prefix Filters, to decrease the number of candidate paths. Experiments show that our Bloom filter-based method takes less time to build routing table than automaton-based method. And our method has a good performance with acceptable false positive when filtering XML packets of relatively small depth with millions of path queries.
{"title":"Bloom filter-based XML packets filtering for millions of path queries","authors":"Xueqing Gong, Ying Yan, Weining Qian, Aoying Zhou","doi":"10.1109/ICDE.2005.26","DOIUrl":"https://doi.org/10.1109/ICDE.2005.26","url":null,"abstract":"The filtering of XML data is the basis of many complex applications. Lots of algorithms have been proposed to solve this problem. One important challenge is that the number of path queries is huge. It is necessary to take an efficient data structure representing path queries. Another challenge is that these path queries usually vary with time. The maintenance of path queries determines the flexibility and capacity of a filtering system. In this paper, we introduce a novel approximate method for XML data filtering, which uses Bloom filters representing path queries. In this method, millions of path queries can be stored efficiently At the same time, it is easy to deal with the change of these path queries. To improve the filtering performance, we introduce a new data structure, Prefix Filters, to decrease the number of candidate paths. Experiments show that our Bloom filter-based method takes less time to build routing table than automaton-based method. And our method has a good performance with acceptable false positive when filtering XML packets of relatively small depth with millions of path queries.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128522271","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}
Sensor networks and other distributed information systems (such as the Web) must frequently access data that has a high per-attribute acquisition cost, in terms of energy, latency, or computational resources. When executing queries that contain several predicates over such expensive attributes, we observe that it can be beneficial to use correlations to automatically introduce low-cost attributes whose observation will allow the query processor to better estimate die selectivity of these expensive predicates. In particular, we show how to build conditional plans that branch into one or more sub-plans, each with a different ordering for the expensive query predicates, based on the runtime observation of low-cost attributes. We frame the problem of constructing the optimal conditional plan for a given user query and set of candidate low-cost attributes as an optimization problem. We describe an exponential time algorithm for finding such optimal plans, and describe a polynomial-time heuristic for identifying conditional plans that perform well in practice. We also show how to compactly model conditional probability distributions needed to identify correlations and build these plans. We evaluate our algorithms against several real-world sensor-network data sets, showing several-times performance increases for a variety of queries versus traditional optimization techniques.
{"title":"Exploiting correlated attributes in acquisitional query processing","authors":"A. Deshpande, Carlos Guestrin, W. Hong, S. Madden","doi":"10.1109/ICDE.2005.63","DOIUrl":"https://doi.org/10.1109/ICDE.2005.63","url":null,"abstract":"Sensor networks and other distributed information systems (such as the Web) must frequently access data that has a high per-attribute acquisition cost, in terms of energy, latency, or computational resources. When executing queries that contain several predicates over such expensive attributes, we observe that it can be beneficial to use correlations to automatically introduce low-cost attributes whose observation will allow the query processor to better estimate die selectivity of these expensive predicates. In particular, we show how to build conditional plans that branch into one or more sub-plans, each with a different ordering for the expensive query predicates, based on the runtime observation of low-cost attributes. We frame the problem of constructing the optimal conditional plan for a given user query and set of candidate low-cost attributes as an optimization problem. We describe an exponential time algorithm for finding such optimal plans, and describe a polynomial-time heuristic for identifying conditional plans that perform well in practice. We also show how to compactly model conditional probability distributions needed to identify correlations and build these plans. We evaluate our algorithms against several real-world sensor-network data sets, showing several-times performance increases for a variety of queries versus traditional optimization techniques.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129358476","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}
Most data integration applications require a matching between the schemas of the respective data sets. We show how the existence of duplicates within these data sets can be exploited to automatically identify matching attributes. We describe an algorithm that first discovers duplicates among data sets with unaligned schemas and then uses these duplicates to perform schema matching between schemas with opaque column names. Discovering duplicates among data sets with unaligned schemas is more difficult than in the usual setting, because it is not clear which fields in one object should be compared with which fields in the other. We have developed a new algorithm that efficiently finds the most likely duplicates in such a setting. Now, our schema matching algorithm is able to identify corresponding attributes by comparing data values within those duplicate records. An experimental study on real-world data shows the effectiveness of this approach.
{"title":"Schema matching using duplicates","authors":"Alexander Bilke, Felix Naumann","doi":"10.1109/ICDE.2005.126","DOIUrl":"https://doi.org/10.1109/ICDE.2005.126","url":null,"abstract":"Most data integration applications require a matching between the schemas of the respective data sets. We show how the existence of duplicates within these data sets can be exploited to automatically identify matching attributes. We describe an algorithm that first discovers duplicates among data sets with unaligned schemas and then uses these duplicates to perform schema matching between schemas with opaque column names. Discovering duplicates among data sets with unaligned schemas is more difficult than in the usual setting, because it is not clear which fields in one object should be compared with which fields in the other. We have developed a new algorithm that efficiently finds the most likely duplicates in such a setting. Now, our schema matching algorithm is able to identify corresponding attributes by comparing data values within those duplicate records. An experimental study on real-world data shows the effectiveness of this approach.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126561766","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}
The focal goal of our research is to develop a general framework which can automatically analyze the sports video, detect the sports events, and finally offer an efficient and user-friendly system for sports video retrieval. In our earlier work, a novel multimedia data mining technique was proposed for automatic soccer event extraction by adopting multimodal feature analysis. Until now, this framework has been performed on the detection of goal and corner kick events and the results are quite impressive. Correspondingly, in this work, the detected video events are modeled and effectively stored in the database. A temporal query model is designed to satisfy the comprehensive temporal query requirements, and the corresponding graphical query language is developed. The advanced characteristics make our model particularly well suited for searching events in a large scale video database.
{"title":"An enhanced query model for soccer video retrieval using temporal relationships","authors":"Shu‐Ching Chen, M. Shyu, Na Zhao","doi":"10.1109/ICDE.2005.20","DOIUrl":"https://doi.org/10.1109/ICDE.2005.20","url":null,"abstract":"The focal goal of our research is to develop a general framework which can automatically analyze the sports video, detect the sports events, and finally offer an efficient and user-friendly system for sports video retrieval. In our earlier work, a novel multimedia data mining technique was proposed for automatic soccer event extraction by adopting multimodal feature analysis. Until now, this framework has been performed on the detection of goal and corner kick events and the results are quite impressive. Correspondingly, in this work, the detected video events are modeled and effectively stored in the database. A temporal query model is designed to satisfy the comprehensive temporal query requirements, and the corresponding graphical query language is developed. The advanced characteristics make our model particularly well suited for searching events in a large scale video database.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125959158","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}
Data warehousing and on-line analytical processing (OLAP) are essential for decision support applications. Common OLAP operations include for example drill down, roll up, pivot and unpivot. Typically, such queries are fairly complex and are often executed over huge volumes of data. The solution in practice is to use materialized views to reduce the query cost. Utilizing materialized views that incorporate not just traditional simple SELECT-PROJECT-JOIN operators but also complex OLAP operators such as pivot and unpivot is crucial to improve the OLAP query performance but as of now unexplored topic. In this work, we demonstrate that the efficient maintenance of views with pivot and unpivot operators requires the definition of more generalized operators, which we call GPIVOT and GUNPIVOT. We propose rewriting rules, combination rules and propagation rules for such operators. We also design a novel view maintenance framework for applying these rules to obtain an efficient maintenance plan. Our query transformation rules are thus dual purpose serving both view maintenance and query optimization. This paves the way for the inclusion of the GPIVOT and GUNPIVOT into any DBMS engine.
{"title":"GPIVOT: efficient incremental maintenance of complex ROLAP views","authors":"Songting Chen, Elke A. Rundensteiner","doi":"10.1109/ICDE.2005.71","DOIUrl":"https://doi.org/10.1109/ICDE.2005.71","url":null,"abstract":"Data warehousing and on-line analytical processing (OLAP) are essential for decision support applications. Common OLAP operations include for example drill down, roll up, pivot and unpivot. Typically, such queries are fairly complex and are often executed over huge volumes of data. The solution in practice is to use materialized views to reduce the query cost. Utilizing materialized views that incorporate not just traditional simple SELECT-PROJECT-JOIN operators but also complex OLAP operators such as pivot and unpivot is crucial to improve the OLAP query performance but as of now unexplored topic. In this work, we demonstrate that the efficient maintenance of views with pivot and unpivot operators requires the definition of more generalized operators, which we call GPIVOT and GUNPIVOT. We propose rewriting rules, combination rules and propagation rules for such operators. We also design a novel view maintenance framework for applying these rules to obtain an efficient maintenance plan. Our query transformation rules are thus dual purpose serving both view maintenance and query optimization. This paves the way for the inclusion of the GPIVOT and GUNPIVOT into any DBMS engine.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"246 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127492291","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}
Nowadays many data mining algorithms focus on clustering methods. There are also a lot of approaches designed for outlier detection. We observe that, in many situations, clusters and outliers are concepts whose meanings are inseparable to each other, especially for those data sets with noise. Thus, it is necessary to treat clusters and outliers as concepts of the same importance in data analysis. In this paper, we present a cluster-outlier iterative detection algorithm, tending to detect the clusters and outliers in another perspective for noisy data sets. In this algorithm, clusters are detected and adjusted according to the intra-relationship within clusters and the inter-relationship between clusters and outliers, and vice versa. The adjustment and modification of the clusters and outliers are performed iteratively until a certain termination condition is reached. This data processing algorithm can be applied in many fields such as pattern recognition, data clustering and signal processing. Experimental results demonstrate the advantages of our approach.
{"title":"Towards exploring interactive relationship between clusters and outliers in multi-dimensional data analysis","authors":"Yong Shi, A. Zhang","doi":"10.1109/ICDE.2005.146","DOIUrl":"https://doi.org/10.1109/ICDE.2005.146","url":null,"abstract":"Nowadays many data mining algorithms focus on clustering methods. There are also a lot of approaches designed for outlier detection. We observe that, in many situations, clusters and outliers are concepts whose meanings are inseparable to each other, especially for those data sets with noise. Thus, it is necessary to treat clusters and outliers as concepts of the same importance in data analysis. In this paper, we present a cluster-outlier iterative detection algorithm, tending to detect the clusters and outliers in another perspective for noisy data sets. In this algorithm, clusters are detected and adjusted according to the intra-relationship within clusters and the inter-relationship between clusters and outliers, and vice versa. The adjustment and modification of the clusters and outliers are performed iteratively until a certain termination condition is reached. This data processing algorithm can be applied in many fields such as pattern recognition, data clustering and signal processing. Experimental results demonstrate the advantages of our approach.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128443485","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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