Native Storage Research Articles

Graph Compression Storage Based on Spatial Cluster Entity Optimization

Graph storage technology is confronted with an enormous challenge as far as the compact and complex graph-structure data. This phenomenon is derived from social networks with spatially intensive data. Since a hot event can cause the generation of a network cluster, which consists of a massive duplicate associated entities in the social networks, the space utilization and processing speed of graph data is obstructed. Therefore, it is necessary to design a graph storage mechanism specifically for the above data. In this paper, we propose a G raph compression S torage engine based on spatial C luster entity O ptimization ( GSCO ), which improves the native graph storage model through the proposed the many-to-one mapping structure and a H eat E volution E limination algorithm ( H2E ). Firstly, we define the spatial cluster entity formally and confirm the compressed storage objects. Then, we introduce the many-to-one relationship to transfer the mapping structure between the node and property. It compresses the data to raise the space utilization of the graph database. Finally, we propose the H2E algorithm that allows the representative nodes to be anchored an extended period in memory according to the heat evolution acceleration. It increases the hit rate and throughput and reduces the I/O operation by deleting the redundancy of data. Extensive experiments results show that the proposed GSCO storage model is better than Neo4j for reading and writing data in spatial clustering entity. It significantly promotes the effectiveness of graph operation, including the data loading, the common queries, and the clustering test.

Custom Block Chain Based Cyber Physical System for Solid Waste Management

The aim of this paper is to develop an intelligent cyber-physical system for the waste management in a locality which will not only keep records of the use of the trash bins in the but also ensure they have an additional blockchain based verification system which will ensure the proper working even in the presence of an adversary. The proposed system is an ensemble of the database server, blockchain server, embedded system based clients and an android app. This is inspired by the recent global trend in smartphone popularity which has piqued interest in the above mentioned division. The advent of smartphone popularity helps us to create an ideal system for a plethora of reasons such as processing capability and native storage etc. Furthermore, blockchain technology, introduced by Satoshi Nakamoto in 2009 ensures decentralization and security of transactions due to its use of cryptographic hash functions and public key cryptographic system. To further excite the situation, India recently revised its solid waste management policy in 2016 after 16 long years. All of the above factors together pointed to creation of a smart waste management system to prevent further exacerbation of India’s existing waste management crisis.

SQL to SPARQL Conversion for Direct RDF Querying

With the advances in native storage means of RDF data and associated querying capabilities using SPARQL, there is a need to let SQL users benefit from such capabilities for interoperability objectives and without any conversion of the RDF data into relational data. In this sense, this work present SQL2SPARQL4RDF an automatic conversion algorithm of SQL queries into SPARQL queries for querying RDF data, which extends the previously established algorithm with relevant SQL elements such as queries with INSERT, DELETE, GROUP BY and HAVING clauses. SQL users are provided with a relational schema of their RDF data against which they can formulate their SQL queries that are then converted into SPARQL equivalent ones with respect to the provided schema. This avoids the birding of translating instances and data replication and thus saving load-ing times and guaranteeing fast execution especially in the case of massive amounts of data. In addition, the automatic mapping framework developed by the java programming language, and implement many new mapping functionalities. Furthermore, to test and validate the efficiency of the mapping approach and adding a module for automatic execution and evaluation of the various obtained SPARQL queries on Allegrograph.

Injectable Silk Protein Microparticle-based Fillers: A Novel Material for Potential Use in Glottic Insufficiency

A novel, silk protein-based injectable filler was engineered with the intention of vocal fold augmentation as its eventual intended use. This injectable filler leverages the unique properties of silk protein's superior biocompatibility, mechanical tunability, and slow in vivo degradation to one day better serve the needs of otolaryngologists. This paper intends to demonstrate the mechanical properties of the proposed novel injectable and to evaluate its longevity in animal models. Experimental. The mechanical properties of silk bulking agents were determined to characterize deformation resistance and recovery compared with commercially available calcium hydroxylapatite through rheologic testing. Fresh porcine vocal fold tissue was used for injectable placement to simulate the mechanical outcomes of native tissue after bulking procedures. In vivo subcutaneous rodent implantation examined immune response, particle migration, and volume retention. Porous, elastomeric silk microparticles demonstrate high recovery (>90% original volume) from compressive strain and mimic the native storage modulus of soft tissues (1-3 kPa). Injectable silk causes only a slight increase in porcine vocal fold stiffness immediately after injection (20%), preserving the native mechanics of bulked tissue. In the subcutaneous rat model, silk demonstrated biocompatibility and slow degradation, thus enabling host cell integration and tissue deposition. The presented novel silk injectable material demonstrates favorable qualities for a vocal fold injection augmentation material. An in vivo long-term canine study is planned.

SQL/JSON Standard: Properties and Deficiencies

Recently, a new era of application development is emerging, which is based upon the ease of access to modern compute resources, such as mobile devices. This access can be supported using JSON (Java Script Object Notation). Therefore, the support of storage and query access for JSON documents in the context of relational DBMSs is necessary. For this reason, the SQL standardization committee published a proposal called SQL/JSON. In this paper we discuss the JSON features specified in the proposal and show to what extent different relational database systems have integrated them. At the end of the paper we describe the main drawbacks of the proposal and the ways to solve them. From our point of view, the following should be specified in one of the future proposals of SQL/JSON: JSON documents should be first-class objects in SQL (native storage). Handling JSON documents as first-class objects in SQL would provide the potential for greater capability for users and for better performance. The support of modification of parts of a JSON document using the SQL UPDATE statement is necessary. Direct access of external JSON data should be supported, too.

Development of an MCNP assisted modelling software based on OpenCasCade

Monte Carlo N-particle transport code (MCNP) is used to calculate the large multi-particle transport in complex three-dimensional (3D) geometry. INP (default name of MCNP input file) file is created by users to solve calculation problems. The manual creation of INP file for MCNP geometry model description is a huge task and mistakes could be easily made. MCNP Assisted Modelling Software (MAMS) proposed in this paper is developed on OpenCasCade geometry engine. It not only achieves the general functions of 3D modelling software, but also performs the conversion of CAD and MCNP models mutually. Furthermore, MAMS implements some advanced functions required by specific users, such as Auxiliary Plane Decomposition (APD), setting the scope of surfaces' number in INP file according to user input. MAMS utilises Standard for the Exchange of Product Model Data (STEP) database as the native storage. It contains two main parts: 3D modelling software and data conversion.

Semantic-Based Requirements Content Management for Cloud Software

Cloud Software is a software complex system whose topology and behavior can evolve dynamically in Cloud-computing environments. Given the unpredictable, dynamic, elasticity, and on-demand nature of the Cloud, it would be unrealistic to assume that traditional software engineering can “cleanly” satisfy the behavioral requirements of Cloud Software. In particular, the majority of traditional requirements managements take document-centric approaches, which have low degree of automation, coarse-grained management, and limited support for requirements modeling activities. Facing the challenges, based on metamodeling frame called RGPS (Role-Goal-Process-Service) international standard, this paper firstly presents a hierarchical framework of semantic-based requirements content management for Cloud Software. And then, it focuses on some of the important management techniques in this framework, such as the native storage scheme, an ordered index with keywords, requirements instances classification based linear conditional random fields (CRFs), and breadth-first search algorithm for associated instances. Finally, a prototype tool called RGPS-RM for semantic-based requirements content management is implemented to provide supporting services for open requirements process of Cloud Software. The proposed framework applied to the Cloud Software development is demonstrated to show the validity and applicability. RGPS-RM also displays effect of fine-grained retrieval and breadth-first search algorithm for associated instance in visualization.

Buffered FUSE: optimising the Android IO stack for user-level filesystem

In this work, we optimise the Android IO stack for user-level filesystem. Android imposes user-level filesystem over native filesystem partition to provide flexibility in managing the internal storage space and to maintain host compatibility. The overhead of user-level filesystem is prohibitively large and the native storage bandwidth is significantly under-utilised. We overhauled the FUSE layer in the Android platform and propose buffered FUSE (bFUSE) to address the overhead of user-level filesystem. The key technical ingredients of buffered FUSE are: 1) extended FUSE IO size; 2) internal user-level write buffer; 3) independent management thread which performs time-driven FUSE buffer synchronisation. With buffered FUSE, we examined the performances of five different filesystems and three disk scheduling algorithms in a combinatorial manner. With bFUSE on XFS filesystem using the deadline scheduling, we achieved the IO performance improvements of 470% and 419% in Android ICS and JB, respectively, over the existing smartphone device.

Subcellular Accumulation and Modification of Pharmaceutical Proteins in Different Plant Tissues

Many plant species and tissues have been investigated as production and delivery vehicles for recombinant pharmaceutical proteins. Examples include cultured cells, whole aquatic plants and transgenic plants expressing recombinant proteins in their leaves, seeds, fruits or tubers/roots. Each platform has unique properties in terms of production time, environmental containment, scalability and overall costs. Plant tissues also differ in their abilities to sort, modify and accumulate proteins. Seeds are naturally adapted for protein accumulation and possess specialized storage organelles that may be exploited to accumulate recombinant proteins, offering stability both in planta and after harvest. Generally, the post-harvest stabilizing effect offered by storage tissues is advantageous for pharmaceuticals, allowing them to be delivered via the mucosal route because they are better able to withstand the harsh microenvironment when protected by the plant matrix. Native storage organelles such as starch granules, protein storage vacuoles and protein bodies thus offer interesting possibilities for the delivery of vaccines and antibodies, as well as novel storage organelles that can be induced ectopically in non-storage tissues. The specialization and distinct intracellular organization of storage tissues also affect the trafficking and modification of recombinant proteins. The N-glycosylation of recombinant glycoproteins often differs subtly depending on the plant species and tissue, reflecting both the availability of different sets of glycan-modifying enzymes and the compartmentalization of the proteins. Where a specific glycan structure is required, it is therefore important to choose the appropriate plant system as a production platform.

Storage Model of Native XML Database Based on Object-Oriented Database

Storage for XML is a main research of Native XML database. Native XML Database has many different physical storage patterns, and their corresponding storage models are different. Based on defining the conceptions of Native XML Database, storage tactics and storage model, the paper describe the storage model based on object oriented database.

Use of natural-gas compositional tracers to investigate gas migration from a gas storage field

Producers adjacent to a natural-gas storage field claimed that the natural gas they were producing was native gas from the area and not storage gas being pulled from the nearby gas storage field. The objective of this work is to apply a combination of area-specific and generic geochemical fingerprinting techniques to determine the source(s) of the natural gas being produced by third-party producers outside the gas storage field and to determine the extent of storage gas migration beyond geologic faults that lie between the production area and the gas storage field. An extensive set of natural-gas samples from the storage field, observation wells around the field, and third-party wells was analyzed for gas hydrocarbon and nonhydrocarbon compositions, as well as stable carbon isotopic compositions of methane and ethane. Gas chemical compositional data, including concentrations of the natural native gas tracer, helium, and ethane carbon isotope, were used to establish the unique fingerprints of native gas and storage gases (end-member sources) and to compare those end-member-source fingerprints to those of natural gas in the third-party wells. The analysis determined that gas in both the observation wells and third-party wells was, in fact, storage gas.

Using storage organelles for the accumulation and encapsulation of recombinant proteins

Plants have been used to produce many diverse and valuable recombinant proteins, including subunit vaccines, antibodies and antibody fragments, hormones, blood products, cytokines, and enzymes. Different plant species and platforms have been explored as production hosts, each with unique properties in terms of the gene transfer method, production time, environmental containment, scalability, downstream processing strategy, protein folding and accumulation, and overall costs. Seed-based systems have many advantages because they exploit the natural storage properties of seeds, which facilitate batch processing and distribution. Seeds possess specialized storage organelles that may be used to accumulate recombinant proteins, offering stability both in planta and after harvest in the final preparation/formulation. The post-harvest stabilizing effect of seeds allows recombinant subunit vaccines and antibodies to be delivered via the mucosal route because they are better able to withstand this harsh microenvironment when protected by the plant matrix. Native storage organelles such as starch granules and protein bodies offer this protective effect, but protein storage organelles can also be induced ectopically in vegetative tissues. In this paper, we discuss the technical capabilities of storage organelle-based expression platforms and their potential applications.

XML Management for Bioinformatics Applications

Scientific exploration has become a data-intensive process, and increasing amounts of data need to be stored, analyzed, and shared. XML can help address these needs. As concrete examples from systems biology show, native XML storage can be combined with traditional relational databases to offer an effective, usable solution for storing scientific data.

Data storage practices and query processing in XML databases: A survey

With the rapid emergence of XML as a data exchange standard over the Web, storing and querying XML data have become critical issues. The two main approaches to storing XML data are (1) to employ traditional storage such as relational database, object-oriented database and so on, and (2) to create an XML-specific native storage. The storage representation affects the efficiency of query processing. In this paper, firstly, we review the two approaches for storing XML data. Secondly, we review various query optimization techniques such as indexing, labeling and join algorithms to enhance query processing in both approaches. Next, we suggest an indexing classification scheme and discuss some of the current trends in indexing methods, which indicate a clear shift towards hybrid indexing.

Plate tectonic reconstructions with continuously closing plates

We present a new algorithm for modeling a self-consistent set of global plate polygons. Each plate polygon is composed of a finite list of plate margins, all with different Euler poles. We introduce a "continuously closed plate" (CCP), such that, as each margin moves independently, the plate polygon remains closed geometrically as a function of time. This method solves emerging needs in computational geodynamics to combine kinematic with dynamic models. Because they have polygons that are too widely spaced in time and have inconsistent motions between margins and plates, traditional global plate tectonic reconstructions have become inadequate for geodynamics. The CCP algorithm has been incorporated into the GPlates open-source paleogeographic system. The algorithm is a set of procedures and data structures that operate on collections of reconstructed geometric data to form closed plate polygons; the main data structures used for each plate polygon are based on a nested hierarchy of topological elements. Reconstructions with CCPs can be created, edited, visualized, and exported with GPlates. The native storage of the dynamic reconstructions is the GPlates Markup Language, GPML, which uses an XML-based file format called GML. We demonstrate the utility of the CCP method by creating a global reconstruction with continuously closing plates from 140 Ma to the present using data from existing, traditional reconstructions.

Data update mechanism for native XML storage scheme

A native XML storage scheme is directly related to query processing and data update.The current native XML storage schemes are mostly concerned with query processing and rarely involve the support of data update.Different from the update of relational tables,XML update needs to take the document order of nodes into account.A novel update mechanism for native XML storage was presented,which not only maintained the document order of nodes,but also restricted an update operation within one page to ensure the update efficiency.Through the introduction of forward link records and relocated records,the update mechanism kept record storage addresses unchanged when splitting a page to avoid the I/O overhead of index update.A case study was made to demonstrate that the data update mechanism for native XML storage scheme is effective.

System Π: A Native RDF Repository Based on the Hypergraph Representation for RDF Data Model

RDF is the data interchange layer for the Semantic Web. In order to manage the increasing amount of RDF data, an RDF repository should provide not only the necessary scalability and efficiency, but also sufficient inference capabilities. Though existing RDF repositories have made progress towards these goals, there is still ample space for improving the overall performance. In this paper, we propose a native RDF repository, System Π, to pursue a better tradeoff among system scalability, query efficiency, and inference capabilities. System Π takes a hypergraph representation for RDF as the data model for its persistent storage, which effectively avoids the costs of data model transformation when accessing RDF data. Based on this native storage scheme, a set of efficient semantic query processing techniques are designed. First, several indices are built to accelerate RDF data access including a value index, a labeling scheme for transitive closure computation, and three triple indices. Second, we propose a hybrid inference strategy under the pD * semantics to support inference for OWL-Lite with a relatively low computational complexity. Finally, we extend the SPARQL algebra to explicitly express inference semantics in logical query plan by defining some new algebra operators. In addition, MD5 hash value of URI and schema level cache are introduced as practical implementation techniques. The results of performance evaluation on the LUBM benchmark and a real data set show that System Π has a better combined metric value than other comparable systems.

Storing semi-structured data on disk drives

Applications that manage semi-structured data are becoming increasingly commonplace. Current approaches for storing semi-structured data use existing storage machinery; they either map the data to relational databases, or use a combination of flat files and indexes. While employing these existing storage mechanisms provides readily available solutions, there is a need to more closely examine their suitability to this class of data. Particularly, retrofitting existing solutions for semi-structured data can result in a mismatch between the tree structure of the data and the access characteristics of the underlying storage device (disk drive). This study explores various possibilities in the design space of native storage solutions for semi-structured data by exploring alternative approaches that match application data access characteristics to those of the underlying disk drive. For evaluating the effectiveness of the proposed native techniques in relation to the existing solution, we experiment with XML data using the XPathMark benchmark. Extensive evaluation reveals the strengths and weaknesses of the proposed native data layout techniques. While the existing solutions work really well for deep-focused queries into a semi-structured document (those that result in retrieving entire subtrees), the proposed native solutions substantially outperform for the non-deep-focused queries, which we demonstrate are at least as important as the deep-focused. We believe that native data layout techniques offer a unique direction for improving the performance of semi-structured data stores for a variety of important workloads. However, given that the proposed native techniques require circumventing current storage stack abstractions, further investigation is warranted before they can be applied to general-purpose storage systems.

Efficient Preprocesses for Fast Storage and Query Retrieval in Native XML Database

XML (extensible mark-up language) has emerged as one of the popular data representation standards for information storage and exchange. In this paper, we propose an extended INLAB architecture, INLAB2, focusing on preprocessing the XML document for fast native storage and accurate query retrieval. Firstly, we propose our xParse parser to check the well-formedness of an XML document. Next, we use a ( self-end) labeling scheme to encode each element in the XML database, by its positional information, to establish parent-child (P-C) or ancestor-descendant (A-D) relationships between nodes. Subsequently, our TwigINLAB2 algorithm is used to optimize query retrieval. TwigINLAB2 is a generalization of TwigStack, the stack-based algorithm for matching twig query. However, the TwigStack algorithm is efficient for A-D relationship queries only. Thus, in order to overcome this limitation, we enhance query retrieval by utilizing indices to speed up the matching and merging phases. Experimental results indicate that TwigINLAB2 can, on an average, process twig queries 23% better than the TwigStack algorithm and 10% better than TwigINLAB1, in terms of execution time.

OrientX: An integrated, schema based native XML database system

The increasing number of XML repositories has stimulated the design of systems that can store and query XML data efficiently. OrientX, a native XML database system, is designed to meet this requirement. Compared with other native XML databases, OrientX has two main features: First, XML schema is fully supported in OrientX, because schema has been proved to be of great importance to XML data storage, Indexing, query optimization and access control; Second, OrientX is an integrated system that meets various requirements on XML data repository, which includes a native storage subsystem, several XML query evaluators, a composite index manager, a cost-based XQuery Optimizer, an Access Control module and an extension to XQuery1.0 for XML update. The main contributions of OrientX are: a)We have implemented an integrated native XML database system, which supports native storage of XML data, and based on it we can handle XPath& XQuery efficiently; b)In our OrientX system, Schema Information is fully explored to guide the storage, optimization and query processing, which we show can boost the system performance remarkably.