Scalable Structural Similarity Analysis of JSON documents Using MapReduce

TurnSTEP is one of the earliest STEP-compliant CNC systems for turning. Based on the STEP-NC data model formalized as ISO 14649-12 and 121, it is designed to support intelligent and autonomous control of NC machines for e-manufacturing. The present paper introduces the development process and data management capability of TurnSTEP. After the overall architecture of the system is presented, the paper discusses the development process of TurnSTEP. Then, it describes the TurnSTEP repository that enables sharing product information anytime, anywhere, on any platform which is an important requirement for e-manufacturing. In addition, the paper presents a method to express STEP data using XML as a core technology of the repository. Since XML is a standard format for data exchange on the web, it is indispensable for the repository to support XML. This method uses only the extension mechanism supported by XML schema to express complex data structures in EXPRESS. It enables product data to be easily stored and shared across the web. Finally, the paper introduces a translator, using the method, which converts STEP data in the clear text format into XML and vice versa.

The casting process CAD system developed based on the existing three-dimensional CAD software has many inadequacies. This paper innovatively puts forward the steel castings three-dimensional casting process CAD system based on neutral STEP file. In this study, by taking advantage of universal CAD data-exchange standard STEP format for data exchange, a practical three-dimensional CAD system was developed based on Open CASCADE Geometry Kernel. The system has the function of practical casting process modeling, including design and modeling of gating system and riser design and so forth. The system exceeded the deficiencies of the existing casting process CAD system. It resolves the foundry Enterprises dependence and limitations on the type and version of commercial 3D CAD software, enabling to realizethe integration of CAD and CAE on casting process.

With the wide adoption of XML as a standard data representation and exchange format querying XML documents becomes increasingly important. However relational database systems constitute a much more mature technology than what is available for native storage of XML. To bridge the gap one way to manage XML data is to use a commercial relational database system. In this approach users typically first ``shred'' their documents by isolating what they predict to be meaningful fragments then store the individual fragments according to some relational schema and later translate each XML query (e.g. expressed in W3C's XQuery) to SQL queries expressed against the shredded documents.In this paper we propose an alternative approach that builds on relational database technology but shreds XML documents dynamically. This avoids many of the problems in maintaining document order and reassembling compound data from its fragments. We then present an algorithm to translate a significant subset of XQuery into an extended relational algebra that includes operators defined for the structured text datatype. This algorithm can be used as the basis of a sound translation from XQuery to SQL and the starting point for query optimization which is required for XML to be supported by relational database technology.

Information imprecision and uncertainty exist in many real-world applications, and for this reason fuzzy data modeling has been extensively investigated in various database models. In particular, Zadeh’s fuzzy set theory has been identified as a successful technique for modeling imprecise and uncertain information in various database models. This has resulted in numerous contributions, mainly with respect to the popular fuzzy conceptual data models (fuzzy ER/EER model, fuzzy UML data model, and etc.) and fuzzy logical database models (fuzzy relational database model, fuzzy object-oriented database model, and etc.). Also, it is shown that fuzzy set theory is very useful in Web-based business intelligence. Therefore, topics related to the modeling of fuzzy data are considered very interesting in XML since it is the current standard data representation and exchange format over the Web. In particular, to manage fuzzy XML data, it is necessary to integrate fuzzy XML and various fuzzy databases, and various fuzzy database models (fuzzy relational database model and fuzzy object-oriented database model) need to be used for mapping to and from the fuzzy XML models. Therefore, in this chapter, we mainly introduce several fuzzy database models, including fuzzy UML data model, fuzzy relational database model, and fuzzy object-oriented database model. Before that, we briefly introduce some notions of fuzzy set theory.

To integrate CA*-systems with other applications in the world of CIM, one principal approach currently under development is based on feature representation. It enables any CIM component to recognize the higher — level entities-the so-called features-out of a lowerdata exchange format, which might be the internal representation of a CAD system as well as some standard data exchange format. In this paper we present a ‘made-to-measure’ editor for representing features in the higher-level domain specific representation language FEAT-REP — a representation language based on a (feature-) specific attributed node labeled graph grammar. This intelligent tool, shortly called GGD, supports the knowledge engineer during the representation process by structuring the knowledge base using a conceptual language and by verifying several characteristics of the features.

XML is the defacto standard for data exchange applications like those in the WWW. However, due to the limited hardware resources, wireless sensor networks abstain from using verbose data formats like XML. Nevertheless, XML as a standardized data exchange format in wireless sensor networks is a means to support more complex data management and heterogeneous networks. Moreover, XML is a key feature towards service-oriented sensor networks that exchange structured information by using SOAP. Recent work has shown that XML can be compressed to meet the general hardware restrictions of sensor nodes while still supporting updates. In this work we outline the vision and benefits of XML usage in wireless sensor networks, show how to evaluate XML queries in wireless sensor networks and how query results can be compressed to lower the comunication overhead. We therefore present an XPath engine on updateable compressed XML data for sensor nodes and an experimental evaluation showing that the performance of our XPath engine fulfills the requirements of today's applications even on sensor nodes.

The acronym GDS stands for Graphic Data System, was developed by Calma enterprises and became for many years the standard digital file format for data exchange for integrated circuit layout artwork. The second version, GDSII was introduced in 1978. The GDSII circuit description is a collection of structures that may contain geometry or other structure-element information as, for example, the exposure dose. These structures have alphanumeric names up to 32 characters long. A library of these structures is contained within the gds-file.

Summary PSI-MI has been endorsed by the protein informatics community as a standard XML data exchange format for protein-protein interaction datasets. While many public databases support the standard, there is a degree of heterogeneity in the way the proposed XML schema is interpreted and instantiated by different data providers. Analysis of schema instantiation in large collections of XML data is a challenging task that is unsupported by existing tools. In this study we use DescribeX, a novel visualization technique of (semi-)structured XML formats, to quantitatively and qualitatively analyze PSI-MI XML collections at the instance level with the goal of gaining insights about schema usage and to study specific questions such as: adequacy of controlled vocabularies, detection of common instance patterns, and evolution of different data collections. Our analysis shows DescribeX enhances understanding the instance-level structure of PSI-MI data sources and is a useful tool for standards designers, software developers, and PSI-MI data providers.

In the big data era, scalability has become a crucial requirement for any useful computational model. Probabilistic graphical models are very useful for mining and discovering data insights, but they are not scalable enough to be suitable for big data problems. Bayesian Networks particularly demonstrate this limitation when their data is represented using few random variables with a massive set of outcome values for each of them. With hierarchical data — data that is arranged in a treelike structure with several levels — one would expect to see hundreds of thousands or millions of values distributed over even just a small number of levels. When modeling this kind of hierarchical data across large data sets, Bayesian networks become unsuitable for representing the probability distributions for the following reasons: i) each level represents a single random variable with hundreds of thousands of values, ii) the number of levels is usually small, so there are also few random variables, and iii) the structure of the network is predefined since the dependency is modeled top-down from each parent to each of its child nodes. In this paper we propose a scalable probabilistic graphical model to overcome these limitations for massive hierarchical data. We believe the proposed model will lead to an easily-scalable, more readable, and expressive implementation for problems that require probabilistic-based solutions for massive amounts of hierarchical data. We successfully applied this model to solve two different challenging probabilistic-based problems on massive hierarchical data sets for different domains, namely, bioinformatics and latent semantic discovery over search logs.

Multimodal neuroimaging has gained traction in Alzheimer's Disease (AD) diagnosis by integrating information from multiple imaging modalities to enhance classification accuracy. However, effectively handling heterogeneous data sources and overcoming the challenges posed by multiscale transform methods remains a significant hurdle. This article proposes a novel approach to address these challenges. To harness the power of diverse neuroimaging data, we employ a strategy that leverages optimized convolution techniques. These optimizations include varying kernel sizes and the incorporation of instance normalization, both of which play crucial roles in feature extraction from magnetic resonance imaging (MRI) and positron emission tomography (PET) images. Specifically, varying kernel sizes allow us to adapt the receptive field to different image characteristics, enhancing the model's ability to capture relevant information. Furthermore, we employ transposed convolution, which increases spatial resolution of feature maps, and it is optimized with varying kernel sizes and instance normalization. This heightened resolution facilitates the alignment and integration of data from disparate MRI and PET data. The use of larger kernels and strides in transposed convolution expands the receptive field, enabling the model to capture essential cross-modal relationships. Instance normalization, applied to each modality during the fusion process, mitigates potential biases stemming from differences in intensity, contrast, or scale between modalities. This enhancement contributes to improved model performance by reducing complexity and ensuring robust fusion. The performance of the proposed fusion method is assessed on three distinct neuroimaging datasets, which include: Alzheimer's Disease Neuroimaging Initiative (ADNI), consisting of 50 participants each at various stages of AD for both MRI and PET (Cognitive Normal, AD, and Early Mild Cognitive); Open Access Series of Imaging Studies (OASIS), consisting of 50 participants each at various stages of AD for both MRI and PET (Cognitive Normal, Mild Dementia, Very Mild Dementia); and whole-brain atlas neuroimaging (AANLIB) (consisting of 50 participants each at various stages of AD for both MRI and PET (Cognitive Normal, AD). To evaluate the quality of the fused images generated via our method, we employ a comprehensive set of evaluation metrics, including Structural Similarity Index Measurement (SSIM), which assesses the structural similarity between two images; Peak Signal-to-Noise Ratio (PSNR), which measures how closely the generated image resembles the ground truth; Entropy (E), which assesses the amount of information preserved or lost during fusion; the Feature Similarity Indexing Method (FSIM), which assesses the structural and feature similarities between two images; and Edge-Based Similarity (EBS), which measures the similarity of edges between the fused and ground truth images. The obtained fused image is further evaluated using a Mobile Vision Transformer. In the classification of AD vs. Cognitive Normal, the model achieved an accuracy of 99.00%, specificity of 99.00%, and sensitivity of 98.44% on the AANLIB dataset.

InfraGAN: A GAN architecture to transfer visible images to infrared domain

Given a similarity function and a threshold s within a range of [0, 1], a similarity join query between two sets of records returns pairs of records from the two sets, which have similarity values exceeding or equaling s. Similarity joins have received much research attention since it is a fundamental operation related to wide applications such as duplicate detection, data integration, and pattern recognition. Recently, a variant of similarity joins is proposed to avoid the need to set the threshold s, i.e. top-k similarity joins. Since data in many applications are generated as a form of continuous data streams, in this paper, we make the first attempt to solve the problem of top-k similarity joins considering a dynamic environment involving a data stream, named continuous top-k similarity joins. Given a set of records as the query, we continuously maintain the top-k pairs of records, ranked by their similarity values, for the query and the most recent data in a monitored data stream. Two algorithms are proposed to solve this problem. The first one extends an existing approach for static datasets to find the top-k pairs regarding the query and the newly arrived data and then keep the obtained pairs in a candidate set. As a result, the top-k pairs can be found from the candidate set. In the other algorithm, the records in the query are preprocessed to be indexed using a novel data structure. By the structure, the data in the monitored stream can be compared with all records in the query at one time, substantially reducing the processing time of finding the top-k results. A series of experiments are performed to evaluate the two proposed algorithms and the experiment results demonstrate that the algorithm with preprocessing outperforms the other algorithm extended from an existing approach.

A string similarity join finds similar string pairs from two sets of strings, which is frequently found in many applications, such as duplicate detection, data integration and cleaning. Various algorithms have been proposed to address its efficiency issues. Partition-based filtering methods, such as Pass-JOIN, are promising, which quickly screens out possible similar string pairs by searching partitioned parts of a string in another string, in order of increasing length, and then performs similarity verification base on edit-distance. We notice that, filtering with different direction produces different candidate sets, which motivate us using a bi-directional filtering mechanism. This paper proposes a novel bi-directional filtering mechanism to enhance the filtering capability, which pipelines filtered results in forward direction to the process of backward filtering. The substring selection method of Pass-JOIN is adapted for the backward filtering. Experimental results show that the proposed bi-directional filtering algorithm outperforms the origin algorithm on real-world datasets.

Utilization of existing clinical data for improving patient outcomes poses a number of challenging and complex problems involving lack of data integration, the absence of standardization across inhomogeneous data sources and computationally-demanding and time-consuming exploration of very large datasets. In this paper, we will present a robust semantic data integration, standardization and dimensionality reduction method to tackle and solve these problems. Our approach enables the integration of clinical data from diverse sources by resolving canonical inconsistencies and semantic heterogeneity as required by the National Library of Medicine's Unified Medical Language System (UMLS) to produce standardized medical data. Through a combined application of rule-based semantic networks and machine learning, our approach enables a large reduction in dimensionality of the data and thus allows for fast and efficient application of data mining techniques to large clinical datasets. An example application of the techniques developed in our study is presented for the prediction of bariatric surgery outcomes.

Deep web data integration has become the center of many research efforts in the recent few years. Near duplicate detection is very important for deep web integration system, there are seldom researches focusing on integrating deep web Integration and near duplicate detection together. In this paper, we develop a integration system, DWI-ndfree to solve this problem. The wrapper of DWI-ndfree consists of four parts: the form filler, the navigator, the extractor and the near duplicate detector. To find near duplicate records, we propose efficient algorithm CheckNearDuplicate. DWI-ndfree can integrate deep web data with near duplicate free and has been used to execute several web extraction and integration tasks efficiently.