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semi-structured data protection scheme based on robust watermarking

EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2020年第1期2020卷 1-10页

作者： He, Jiahuan Ying, Qichao Qian, Zhenxing Feng, Guorui Zhang, Xinpeng Shanghai Univ Sch Commun & Informat Engn Shanghai Peoples R China Fudan Univ Shanghai Inst Intelligent Elect & Syst Sch Comp Sci Shanghai Peoples R China

semi-structured data is a widely used text format for data interchange and storage. This paper proposes a robust watermarking scheme of data protection for semi-structured data, which uses JSON format as an example for illustration. We first parse JSON file into a data structure of distinct pairs. Afterwards, we generate a transfer matrix to get the intermediate sequences, which are then encoded using error-correction codes and embedded into the pairs. A private key is shared by the data hider and the recipient to resist collusion attacks. On the recipient's side, data extraction can be successfully carried out even the received stego data are tampered. The imperceptibility is realized by embedding data into the less significant digits of numeric data in the cover file. The proposed scheme can be extended on several other formats. The experimental results show that the proposed scheme is robust to various kinds of typical attacks such as contextual truncating, modification, and redundancy addition.

关键词： Copyright protection Collusion attack semi-structured data Watermarking

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semi-structured data Extraction and Schema Knowledge Mining

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High Technology Letters 2001年第1期7卷 1-5页

作者：陈恩红 WANG Xufa Department of Computer Science University of Science and Technology ofChina

A semi structured data extraction method to get the useful information embedded in a group of relevant web pages and store it with OEM(Object Exchange Model) is proposed. Then, the data mining method is adopted to discover schema knowledge implicit in the semi structured data. This knowledge can make users understand the information structure on the web more deeply and thourouly. At the same time, it can also provide a kind of effective schema for the querying of web information.

关键词： semi-structured data Schema data extraction.

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semi-structured data Model for Big data (SS-DMBD) 8

Semi-Structured Data Model for Big Data (SS-DMBD)

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8th International Conference on data Science, Technology and Applications (data)

作者： Hamouda, Shady Zainol, Zurinahni Univ Sains Malaysia George Town Malaysia Emirates Coll Technol Abu Dhabi U Arab Emirates

ISBN: (纸本)9789897583773

New business applications require flexibility in data model structure and must support the next generation of web applications and handle complex data types. The performance of processing structured data through a relational database has become incompatible with big data challenges. Nowadays, there is a need to deal with semi-structured data with a flexible schema for different applications. Not only SQL (NoSQL) has been presented to overcome the limitations of relational databases in terms of scale, performance, data model, and distribution system. Also, NoSQL supports semi-structured data and can handle a huge amount of data and provide flexibility in the data schema. But the data models of NoSQL systems are very complex, as there are no tools available to represent a scheme for NoSQL databases. In addition, there is no standard schema for data modelling of document-oriented databases. This study proposes a semi-structured data model for big data (SS-DMBD) that is compatible with a document-oriented database, and also proposes an algorithm for mapping the entity relationship (ER) model to SS-DMBD. A case study is used to evaluate the SS-DMBD and its features. The results show that this model can address most features of semi- structured data.

关键词： semi-structured data Document-oriented database Big data NoSQL

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semi-structured data Dynamic Integration Mashup System 11

Semi-Structured Data Dynamic Integration Mashup System

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XIth International Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT)

作者： Kushniretska, Irina Lviv Polytech Natl Univ Informat Syst & Networks Dept Lvov Ukraine

The principles of functioning of the semi-structured data dynamic integration Mashup system have been described. The modern technologies of Mashup data integration realization have been considered. The basic processes... 详细信息

ISBN: (纸本)9781509027392

关键词： Mashup system dynamic integration data processing semi-structured data

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A Survey on Mapping semi-structured data and Graph data to Relational data

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ACM COMPUTING SURVEYS 2023年第10期55卷 1-38页

作者： Yuan, Gongsheng Lu, Jiaheng Yan, Zhengtong Wu, Sai Zhejiang Univ Hangzhou 310027 Peoples R China Univ Helsinki Helsinki 00014 Finland

The data produced by various services should be stored and managed in an appropriate format for gaining valuable knowledge conveniently. This leads to the emergence of various data models, including relational, semi-structured, and graph models, and so on. Considering the fact that the mature relational databases established on relational data models are still predominant in today's market, it has fueled interest in storing and processing semi-structured data and graph data in relational databases so that mature and powerful relational databases' capabilities can all be applied to these various data. In this survey, we review existing methods on mapping semi-structured data and graph data into relational tables, analyze their major features, and give a detailed classification of those methods. We also summarize the merits and demerits of each method, introduce open research challenges, and present future research directions. With this comprehensive investigation of existing methods and open problems, we hope this survey can motivate new mapping approaches through drawing lessons from eachmodel's mapping strategies, aswell as a newresearch topic - mapping multi-model data into relational tables.

关键词： Relational schema relational storage semi-structured data JSON XML graph data RDF property graph model mapping

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Discovering OLAP dimensions in semi-structured data

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INFORMATION SYSTEMS 2014年 44卷 120-133页

作者： Mansmann, Svetlana Rehman, Nafees Ur Weiler, Andreas Scholl, Marc H. Univ Konstanz D-78457 Constance Germany

OLAP cubes enable aggregation-centric analysis of transactional data by shaping data records into measurable facts with dimensional characteristics. A multidimensional view is obtained from the available data fields and explicit relationships between them. This classical modeling approach is not feasible for scenarios dealing with semi-structured or poorly structured data. We propose to the data warehouse design methodology with a content-driven discovery of measures and dimensions in the original dataset. Our approach is based on introducing a data enrichment layer responsible for detecting new structural elements in the data using data mining and other techniques. Discovered elements can be of type measure, dimension, or hierarchy level and may represent static or even dynamic properties of the data. This paper focuses on the challenge of generating, maintaining, and querying discovered elements in OLAP cubes. We demonstrate the power of our approach by providing OLAP to the public stream of user-generated content on the Twitter platform. We have been able to enrich the original set with dynamic characteristics, such as user activity, popularity, messaging behavior, as well as to classify messages by topic, impact, origin, method of generation, etc. Knowledge discovery techniques coupled with human expertise enable structural enrichment of the original data beyond the scope of the existing methods for obtaining multidimensional models from relational or semi-structured data. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： data warehousing OLAP Multidimensional data model semi-structured data

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Automating data Mart Construction from semi-structured data Sources

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COMPUTER JOURNAL 2019年第3期62卷 394-413页

作者： Scriney, Michael McCarthy, Suzanne McCarren, Andrew Cappellari, Paolo Roantree, Mark Dublin City Univ Sch Comp Insight Ctr Data Analyt Dublin 9 Ireland CUNY Informat Syst & Informat Staten Isl NY 10314 USA

The global food and agricultural industry has a total market value of USD 8 trillion in 2016, and decision makers in the Agri sector require appropriate tools and up-to-date information to make predictions across a range of products and areas. Traditionally, these requirements are met with information processed into a data warehouse and data marts constructed for analyses. Increasingly however, data are coming from outside the enterprise and often in unprocessed forms. As these sources are outside the control of companies, they are prone to change and new sources may appear. In these cases, the process of accommodating these sources can be costly and very time consuming. To automate this process, what is required is a sufficiently robust extract-transform-load process;external sources are mapped to some form of ontology, and an integration process to merge the specific data sources. In this paper, we present an approach to automating the integration of data sources in an Agri environment, where new sources are examined before an attempt to merge them with existing data marts. Our validation uses a case study of real world Agri data to demonstrate the robustness of our approach and the efficiency of materializing data marts.

关键词： data model transformation semi-structured data ETL data marts

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A method of semi-automated ontology population from multiple semi-structured data sources

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JOURNAL OF INFORMATION SCIENCE 2022年第2期48卷 223-236页

作者： Leshcheva, Irina Begler, Alena St Petersburg Univ Grad Sch Management Volkhovsky Per 3 St Petersburg 199004 Russia

Organisations use data in different formats: Word documents, Excel spreadsheets, databases, HTML pages and so on. It is not easy to make decisions with such data due to the lack of integration between the different sources and built-in decision-making rules. Decisions can be reached with knowledge bases, which, unlike databases, make it possible to store not only objects, facts and attributes but also more sophisticated patterns such as rules and axioms. The article proposes an ontology-based method for knowledge base creation that allows for the simultaneous integration of semi-structured data sources and extendibility while remaining context independent. At the initial steps of the method, data specification should be performed with the data Sources Ontology developed by the authors. This ontology provides data structure description that forms supportive knowledge graph. The graph's schema should be mapped with the domain ontology to be populated. Finally, the data are inserted into the domain ontology according to the mapping rules. Manual input is needed during data specification and data-to-ontology schema mapping.

关键词： data Source Ontology ontology-based data integration ontology population semi-structured data

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A partition index for XML and semi-structured data

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data & KNOWLEDGE ENGINEERING 2004年第3期51卷 349-368页

作者： Kim, J Kim, HJ Seoul Natl Univ Sch Comp Sci & Engn Kwanak Gu Seoul 151742 South Korea

XML and other semi-structured data can be represented by a graph model. The paths in a data graph are used as a basic constructor of a query. Especially, by using patterns on paths, a user can formulate more expressive queries. Patterns in a path enlarge the search space of a data graph and current research for indexing semi-structured data focuses on reducing the search space. However, the existing indexes cannot reduce the search space when a data graph has some references. In this paper, we introduce a partitioning technique for all paths in a data graph and an index graph which can effectively find appropriate path partitions for a path query with patterns. (C) 2004 Elsevier B.V. All rights reserved.

关键词： XML semi-structured data path query path partition

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A distributed selectivity-driven search strategy for semi-structured data over DHT-based networks

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2016年 93-94卷 10-29页

作者： Comito, Carmela Talia, Domenico Trunfio, Paolo ICAR CNR Arcavacata Di Rende CS Italy Univ Calabria DIMES Arcavacata Di Rende CS Italy

Distributed Hash Tables (DHTs) are widely used for indexing and locating many types of resources, including semi-structured data modeled as XML documents. A common distributed strategy to process an XML query over a DHT consists in splitting it into a set of simple path queries, and resolving each of them separately. The traffic generated by this strategy grows with the number of paths in the query. To overcome this drawback, an alternative strategy consists in resolving only the sub-query associated with the most selective path, and then submitting the original query to the nodes in the result set. A first goal of this paper is to provide an analytical and experimental study of the two strategies to assess their relative merits in different scenarios. On the basis of this study, we introduce an Adaptive Path Selection (APS) search technique that resolves an XML query in a distributed way by querying either the most selective path or the whole path set, based on the selectivity of the paths in the query. The effective use of APS requires that the querying nodes know in advance the selectivity of all the paths. Addressing this problem is another goal of the paper, which is achieved through: (i) The definition of a space-efficient data structure, the Path Selectivity Table (PST), which given any path, returns an estimate of its selectivity. (ii) The definition of an efficient strategy that builds the PST in a distributed way and propagates it to all nodes in the network with logarithmic performance bounds and without redundant messages. Experimental results show that the PST accurately estimates the path selectivity values, and that the traffic generated by the APS algorithm using PST-estimated selectivity values is comparable to that produced by APS assuming to know the real path selectivity values. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Distributed Hash Tables semi-structured data Path selectivity Adaptive Path Selection

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