Database outsourcing, whilst becoming more popular in recent years, is creating substantial security and privacy risks. In this paper, we assess cryptographic solutions to the problem that some client party (Alex) wants to outsource database operations on sensitive data sets to a service provider (Eve) without having to trust her. Contracts are an option, but for various reasons their effectiveness is limited [2]. Alex would rather like to use privacy homomorphisms [6], i.e., encryption schemes that transform relational data sets and queries into ciphertext such that (i) the data is securely hidden from Eve; and (ii) Eve computes hidden results from hidden queries that Alex can efficiently decrypt. Unfortunately, all privacy homomorphisms we know of lack a rigorous security analysis. Before they can be used in practice, we need formal definitions that are both sound and practical to assess their effectiveness.
{"title":"Provable Security for Outsourcing Database Operations","authors":"S. Evdokimov, M. Fischmann, O. Günther","doi":"10.1109/ICDE.2006.121","DOIUrl":"https://doi.org/10.1109/ICDE.2006.121","url":null,"abstract":"Database outsourcing, whilst becoming more popular in recent years, is creating substantial security and privacy risks. In this paper, we assess cryptographic solutions to the problem that some client party (Alex) wants to outsource database operations on sensitive data sets to a service provider (Eve) without having to trust her. Contracts are an option, but for various reasons their effectiveness is limited [2]. Alex would rather like to use privacy homomorphisms [6], i.e., encryption schemes that transform relational data sets and queries into ciphertext such that (i) the data is securely hidden from Eve; and (ii) Eve computes hidden results from hidden queries that Alex can efficiently decrypt. Unfortunately, all privacy homomorphisms we know of lack a rigorous security analysis. Before they can be used in practice, we need formal definitions that are both sound and practical to assess their effectiveness.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"63 4 1","pages":"117-117"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83890578","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}
A mobile ad-hoc network (MANET) is a set of moving objects that communicate with each other via unregulated, short-range wireless technologies such as IEEE 802.11, Bluetooth, or Ultra Wide Band (UWB). No fixed infrastructure is assumed or relied upon. An important application domain of MANET’s is local resource discovery. In a local resource discovery application, a user finds local resources that satisfy specified criteria. For example, a driver finds an available parking slot in a region by receiving information generated by the parking meter, or gets the traffic conditions on a highway segment a mile ahead; a cab driver finds a near-by customer, or a participant at a convention finds another participant with a matching profile.
{"title":"Searching Local Information in Mobile Databases","authors":"O. Wolfson, Bo Xu, Huabei Yin, Hu Cao","doi":"10.1109/ICDE.2006.135","DOIUrl":"https://doi.org/10.1109/ICDE.2006.135","url":null,"abstract":"A mobile ad-hoc network (MANET) is a set of moving objects that communicate with each other via unregulated, short-range wireless technologies such as IEEE 802.11, Bluetooth, or Ultra Wide Band (UWB). No fixed infrastructure is assumed or relied upon. An important application domain of MANET’s is local resource discovery. In a local resource discovery application, a user finds local resources that satisfy specified criteria. For example, a driver finds an available parking slot in a region by receiving information generated by the parking meter, or gets the traffic conditions on a highway segment a mile ahead; a cab driver finds a near-by customer, or a participant at a convention finds another participant with a matching profile.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"1 1","pages":"136-136"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88226614","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}
Counting in general, and estimating the cardinality of (multi-) sets in particular, is highly desirable for a large variety of applications, representing a foundational block for the efficient deployment and access of emerging internetscale information systems. Examples of such applications range from optimizing query access plans in internet-scale databases, to evaluating the significance (rank/score) of various data items in information retrieval applications. The key constraints that any acceptable solution must satisfy are: (i) efficiency: the number of nodes that need be contacted for counting purposes must be small in order to enjoy small latency and bandwidth requirements; (ii) scalability, seemingly contradicting the efficiency goal: arbitrarily large numbers of nodes nay need to add elements to a (multi-) set, which dictates the need for a highly distributed solution, avoiding server-based scalability, bottleneck, and availability problems; (iii) access and storage load balancing: counting and related overhead chores should be distributed fairly to the nodes of the network; (iv) accuracy: tunable, robust (in the presence of dynamics and failures) and highly accurate cardinality estimation; (v) simplicity and ease of integration: special, solution-specific indexing structures should be avoided. In this paper, first we contribute a highly-distributed, scalable, efficient, and accurate (multi-) set cardinality estimator. Subsequently, we show how to use our solution to build and maintain histograms, which have been a basic building block for query optimization for centralized databases, facilitating their porting into the realm of internet-scale data networks.
{"title":"Counting at Large: Efficient Cardinality Estimation in Internet-Scale Data Networks","authors":"Nikos Ntarmos, P. Triantafillou, G. Weikum","doi":"10.1109/ICDE.2006.44","DOIUrl":"https://doi.org/10.1109/ICDE.2006.44","url":null,"abstract":"Counting in general, and estimating the cardinality of (multi-) sets in particular, is highly desirable for a large variety of applications, representing a foundational block for the efficient deployment and access of emerging internetscale information systems. Examples of such applications range from optimizing query access plans in internet-scale databases, to evaluating the significance (rank/score) of various data items in information retrieval applications. The key constraints that any acceptable solution must satisfy are: (i) efficiency: the number of nodes that need be contacted for counting purposes must be small in order to enjoy small latency and bandwidth requirements; (ii) scalability, seemingly contradicting the efficiency goal: arbitrarily large numbers of nodes nay need to add elements to a (multi-) set, which dictates the need for a highly distributed solution, avoiding server-based scalability, bottleneck, and availability problems; (iii) access and storage load balancing: counting and related overhead chores should be distributed fairly to the nodes of the network; (iv) accuracy: tunable, robust (in the presence of dynamics and failures) and highly accurate cardinality estimation; (v) simplicity and ease of integration: special, solution-specific indexing structures should be avoided. In this paper, first we contribute a highly-distributed, scalable, efficient, and accurate (multi-) set cardinality estimator. Subsequently, we show how to use our solution to build and maintain histograms, which have been a basic building block for query optimization for centralized databases, facilitating their porting into the realm of internet-scale data networks.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"37 1","pages":"40-40"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88604567","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}
Recent work both in the relational and the XML world have shown that the efficacy and efficiency of duplicate detection is enhanced by regarding relationships between entities. However, most approaches for XML data rely on 1:n parent/child relationships, and do not apply to XML data that represents m:n relationships. We present a novel comparison strategy, which performs duplicate detection effectively for all kinds of parent/child relationships, given dependencies between different XML elements. Due to cyclic dependencies, it is possible that a pairwise classification is performed more than once, which compromises efficiency. We propose an order that reduces the number of such reclassifications and apply it to two algorithms. The first algorithm performs reclassifications, and efficiency is increased by using the order reducing the number of reclassifications. The second algorithm does not perform a comparison more than once, and the order is used to miss few reclassifications and hence few potential duplicates.
{"title":"Detecting Duplicates in Complex XML Data","authors":"Melanie Herschel, Felix Naumann","doi":"10.1109/ICDE.2006.49","DOIUrl":"https://doi.org/10.1109/ICDE.2006.49","url":null,"abstract":"Recent work both in the relational and the XML world have shown that the efficacy and efficiency of duplicate detection is enhanced by regarding relationships between entities. However, most approaches for XML data rely on 1:n parent/child relationships, and do not apply to XML data that represents m:n relationships. We present a novel comparison strategy, which performs duplicate detection effectively for all kinds of parent/child relationships, given dependencies between different XML elements. Due to cyclic dependencies, it is possible that a pairwise classification is performed more than once, which compromises efficiency. We propose an order that reduces the number of such reclassifications and apply it to two algorithms. The first algorithm performs reclassifications, and efficiency is increased by using the order reducing the number of reclassifications. The second algorithm does not perform a comparison more than once, and the order is used to miss few reclassifications and hence few potential duplicates.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"27 16","pages":"109-109"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91434133","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}
We study the problem of answering queries through a RDF/RDFS ontology, given a set of view-based mappings between one or more relational schemas and this target ontology. Particularly, we consider a set of RDFS semantic constraints such as rdfs:subClassof, rdfs:subPropertyof, rdfs:domain, and rdfs:range, which are present in RDF model but neither XML nor relational models. We formally define the query semantics in such an integration scenario, and design a novel query rewriting algorithm to implement the semantics. On our approach, we highlight the important role played by RDF Blank Node in representing incomplete semantics of relational data. A set of semantic tools supporting relational data integration by RDF are also introduced. The approach have been used to integrate 70 relational databases at China Academy of Traditional Chinese Medicine.
{"title":"RDF/RDFS-based Relational Database Integration","authors":"Huajun Chen, Zhaohui Wu, Heng Wang, Yuxin Mao","doi":"10.1109/ICDE.2006.127","DOIUrl":"https://doi.org/10.1109/ICDE.2006.127","url":null,"abstract":"We study the problem of answering queries through a RDF/RDFS ontology, given a set of view-based mappings between one or more relational schemas and this target ontology. Particularly, we consider a set of RDFS semantic constraints such as rdfs:subClassof, rdfs:subPropertyof, rdfs:domain, and rdfs:range, which are present in RDF model but neither XML nor relational models. We formally define the query semantics in such an integration scenario, and design a novel query rewriting algorithm to implement the semantics. On our approach, we highlight the important role played by RDF Blank Node in representing incomplete semantics of relational data. A set of semantic tools supporting relational data integration by RDF are also introduced. The approach have been used to integrate 70 relational databases at China Academy of Traditional Chinese Medicine.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"70 1","pages":"94-94"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90392563","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}
Kun Gao, S. Harizopoulos, I. Pandis, Vladislav Shkapenyuk, A. Ailamaki
Data warehousing and scientific database applications operate on massive datasets and are characterized by complex queries accessing large portions of the database. Concurrent queries often exhibit high data and computation overlap, e.g., they access the same relations on disk, compute similar aggregates, or share intermediate results. Unfortunately, run-time sharing in modern database engines is limited by the paradigm of invoking an independent set of operator instances per query, potentially missing sharing opportunities if the buffer pool evicts data early.
{"title":"Simultaneous Pipelining in QPipe: Exploiting Work Sharing Opportunities Across Queries","authors":"Kun Gao, S. Harizopoulos, I. Pandis, Vladislav Shkapenyuk, A. Ailamaki","doi":"10.1109/ICDE.2006.138","DOIUrl":"https://doi.org/10.1109/ICDE.2006.138","url":null,"abstract":"Data warehousing and scientific database applications operate on massive datasets and are characterized by complex queries accessing large portions of the database. Concurrent queries often exhibit high data and computation overlap, e.g., they access the same relations on disk, compute similar aggregates, or share intermediate results. Unfortunately, run-time sharing in modern database engines is limited by the paradigm of invoking an independent set of operator instances per query, potentially missing sharing opportunities if the buffer pool evicts data early.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"1 1","pages":"162-162"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85914842","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}
Feifei Li, Ching Chang, G. Kollios, Azer Bestavros
In this paper, we investigate a new approach to process queries in data stream applications. We show that reference locality characteristics of data streams could be exploited in the design of superior and flexible data stream query processing techniques. We identify two different causes of reference locality: popularity over long time scales and temporal correlations over shorter time scales. An elegant mathematical model is shown to precisely quantify the degree of those sources of locality. Furthermore, we analyze the impact of locality-awareness on achievable performance gains over traditional algorithms on applications such asMAX-subset approximate sliding window join and approximate count estimation. In a comprehensive experimental study, we compare several existing algorithms against our locality-aware algorithms over a number of real datasets. The results validate the usefulness and efficiency of our approach.
{"title":"Characterizing and Exploiting Reference Locality in Data Stream Applications","authors":"Feifei Li, Ching Chang, G. Kollios, Azer Bestavros","doi":"10.1109/ICDE.2006.33","DOIUrl":"https://doi.org/10.1109/ICDE.2006.33","url":null,"abstract":"In this paper, we investigate a new approach to process queries in data stream applications. We show that reference locality characteristics of data streams could be exploited in the design of superior and flexible data stream query processing techniques. We identify two different causes of reference locality: popularity over long time scales and temporal correlations over shorter time scales. An elegant mathematical model is shown to precisely quantify the degree of those sources of locality. Furthermore, we analyze the impact of locality-awareness on achievable performance gains over traditional algorithms on applications such asMAX-subset approximate sliding window join and approximate count estimation. In a comprehensive experimental study, we compare several existing algorithms against our locality-aware algorithms over a number of real datasets. The results validate the usefulness and efficiency of our approach.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"16 1","pages":"81-81"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90681257","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}
In a Mobile Ad-hoc Network (MANET), due to the mobility and energy limitations of nodes, disconnection and network partitioning occur frequently. In addition, transactions in many MANET database applications have time constraints. In this paper, a Data REplication technique for real-time Ad-hoc Mobile databases (DREAM) that addresses all these issues is proposed. DREAM is prototyped on laptops and PDAs and compared with two existing replication techniques using a military database application.
{"title":"DREAM: A Data Replication Technique for Real-Time Mobile Ad-hoc Network Databases","authors":"P. Padmanabhan, L. Gruenwald","doi":"10.1109/ICDE.2006.52","DOIUrl":"https://doi.org/10.1109/ICDE.2006.52","url":null,"abstract":"In a Mobile Ad-hoc Network (MANET), due to the mobility and energy limitations of nodes, disconnection and network partitioning occur frequently. In addition, transactions in many MANET database applications have time constraints. In this paper, a Data REplication technique for real-time Ad-hoc Mobile databases (DREAM) that addresses all these issues is proposed. DREAM is prototyped on laptops and PDAs and compared with two existing replication techniques using a military database application.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"15 1","pages":"134-134"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90970035","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}
Haixun Wang, Hao He, Jun Yang, Philip S. Yu, J. Yu
Graph reachability is fundamental to a wide range of applications, including XML indexing, geographic navigation, Internet routing, ontology queries based on RDF/OWL, etc. Many applications involve huge graphs and require fast answering of reachability queries. Several reachability labeling methods have been proposed for this purpose. They assign labels to the vertices, such that the reachability between any two vertices may be decided using their labels only. For sparse graphs, 2-hop based reachability labeling schemes answer reachability queries efficiently using relatively small label space. However, the labeling process itself is often too time consuming to be practical for large graphs. In this paper, we propose a novel labeling scheme for sparse graphs. Our scheme ensures that graph reachability queries can be answered in constant time. Furthermore, for sparse graphs, the complexity of the labeling process is almost linear, which makes our algorithm applicable to massive datasets. Analytical and experimental results show that our approach is much more efficient than stateof- the-art approaches. Furthermore, our labeling method also provides an alternative scheme to tradeoff query time for label space, which further benefits applications that use tree-like graphs.
{"title":"Dual Labeling: Answering Graph Reachability Queries in Constant Time","authors":"Haixun Wang, Hao He, Jun Yang, Philip S. Yu, J. Yu","doi":"10.1109/ICDE.2006.53","DOIUrl":"https://doi.org/10.1109/ICDE.2006.53","url":null,"abstract":"Graph reachability is fundamental to a wide range of applications, including XML indexing, geographic navigation, Internet routing, ontology queries based on RDF/OWL, etc. Many applications involve huge graphs and require fast answering of reachability queries. Several reachability labeling methods have been proposed for this purpose. They assign labels to the vertices, such that the reachability between any two vertices may be decided using their labels only. For sparse graphs, 2-hop based reachability labeling schemes answer reachability queries efficiently using relatively small label space. However, the labeling process itself is often too time consuming to be practical for large graphs. In this paper, we propose a novel labeling scheme for sparse graphs. Our scheme ensures that graph reachability queries can be answered in constant time. Furthermore, for sparse graphs, the complexity of the labeling process is almost linear, which makes our algorithm applicable to massive datasets. Analytical and experimental results show that our approach is much more efficient than stateof- the-art approaches. Furthermore, our labeling method also provides an alternative scheme to tradeoff query time for label space, which further benefits applications that use tree-like graphs.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"26 1","pages":"75-75"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84739777","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}
formulation and efficient processing of the formulated query. However, due to the nature of XML data, formulating an XML query using an XML query language such as XQuery requires considerable effort. A user must be completely familiar with the syntax of the query language, and must be able to express his/her needs accurately in a syntactically correct form. In many real life applications it is not realistic to assume that users are proficient in expressing such textual queries. Hence, there is a need for a user-friendly visual querying schemes to replace data retrieval aspects of XQuery. In this paper, we address the problem of efficient processing of XQueries in the relational environment where the queries are formulated using a user-friendly GUI. We take a novel and non-traditional approach to improving query performance by prefetching data during the formulation of a query in a single-user environment. The latency offered by the GUI-based query formulation is utilized to prefetch portions of the query results. The basic idea we employ for prefetching is that we prefetch constituent path expressions, store the intermediary results, reuse them when connective is added or "Run" is pressed.
{"title":"Every Click You Make, IWill Be Fetching It: Efficient XML Query Processing in RDMS Using GUI-driven Prefetching","authors":"S. Bhowmick, Sandeep Prakash","doi":"10.1109/ICDE.2006.64","DOIUrl":"https://doi.org/10.1109/ICDE.2006.64","url":null,"abstract":"formulation and efficient processing of the formulated query. However, due to the nature of XML data, formulating an XML query using an XML query language such as XQuery requires considerable effort. A user must be completely familiar with the syntax of the query language, and must be able to express his/her needs accurately in a syntactically correct form. In many real life applications it is not realistic to assume that users are proficient in expressing such textual queries. Hence, there is a need for a user-friendly visual querying schemes to replace data retrieval aspects of XQuery. In this paper, we address the problem of efficient processing of XQueries in the relational environment where the queries are formulated using a user-friendly GUI. We take a novel and non-traditional approach to improving query performance by prefetching data during the formulation of a query in a single-user environment. The latency offered by the GUI-based query formulation is utilized to prefetch portions of the query results. The basic idea we employ for prefetching is that we prefetch constituent path expressions, store the intermediary results, reuse them when connective is added or \"Run\" is pressed.","PeriodicalId":6819,"journal":{"name":"22nd International Conference on Data Engineering (ICDE'06)","volume":"45 1","pages":"152-152"},"PeriodicalIF":0.0,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85086503","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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