scholarly journals PlantES: A Plant Electrophysiological Multi-Source Data Online Analysis and Sharing Platform

2018 ◽  
Vol 8 (11) ◽  
pp. 2269 ◽  
Author(s):  
Chao Song ◽  
Xiao-Huang Qin ◽  
Qiao Zhou ◽  
Zi-Yang Wang ◽  
Wei-He Liu ◽  
...  
Keyword(s):  
Data Sharing ◽  
Template Matching ◽  
Distributed Storage ◽  
Prototype System ◽  
Fast Analysis ◽  
Online Analysis ◽  
Electrical Signals ◽  
Electrophysiological Data ◽  
Efficient Management ◽  
Source Data

At present, plant electrophysiological data volumes and complexity are increasing rapidly. It causes the demand for efficient management of big data, data sharing among research groups, and fast analysis. In this paper, we proposed PlantES (Plant Electrophysiological Data Sharing), a distributed computing-based prototype system that can be used to store, manage, visualize, analyze, and share plant electrophysiological data. We deliberately designed a storage schema to manage the multi-source plant electrophysiological data by integrating distributed storage systems HDFS and HBase to access all kinds of files efficiently. To improve the online analysis efficiency, parallel computing algorithms on Spark were proposed and implemented, e.g., plant electrical signals extraction method, the adaptive derivative threshold algorithm, and template matching algorithm. The experimental results indicated that Spark efficiently improves the online analysis. Meanwhile, the online visualization and sharing of multiple types of data in the web browser were implemented. Our prototype platform provides a solution for web-based sharing and analysis of plant electrophysiological multi-source data and improves the comprehension of plant electrical signals from a systemic perspective.

scholarly journals Getting Started Creating Data Dictionaries: How to Create a Shareable Data Set

2021 ◽  
Vol 4 (1) ◽  
pp. 251524592092800
Author(s):  
Erin M. Buchanan ◽  
Sarah E. Crain ◽  
Ari L. Cunningham ◽  
Hannah R. Johnson ◽  
Hannah Stash ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Sharing ◽  
Search Engine ◽  
Web Applications ◽  
Data Sets ◽  
Data Dictionary ◽  
Data Set ◽  
Entire Process ◽  
Shared Data ◽  
Source Data

As researchers embrace open and transparent data sharing, they will need to provide information about their data that effectively helps others understand their data sets’ contents. Without proper documentation, data stored in online repositories such as OSF will often be rendered unfindable and unreadable by other researchers and indexing search engines. Data dictionaries and codebooks provide a wealth of information about variables, data collection, and other important facets of a data set. This information, called metadata, provides key insights into how the data might be further used in research and facilitates search-engine indexing to reach a broader audience of interested parties. This Tutorial first explains terminology and standards relevant to data dictionaries and codebooks. Accompanying information on OSF presents a guided workflow of the entire process from source data (e.g., survey answers on Qualtrics) to an openly shared data set accompanied by a data dictionary or codebook that follows an agreed-upon standard. Finally, we discuss freely available Web applications to assist this process of ensuring that psychology data are findable, accessible, interoperable, and reusable.

Research and Application on Manufacturing Scientific Data Sharing Service Customization

2012 ◽  
Vol 522 ◽  
pp. 770-775
Author(s):  
Yu Zheng ◽  
Yan Rong Ni ◽  
Deng Zhe Ma
Keyword(s):  
Data Sharing ◽  
Scientific Data ◽  
Prototype System ◽  
Data Service ◽  
Service Model ◽  
Resource Model ◽  
Data Resource ◽  
Key Technologies ◽  
Service Customization ◽  
Registration Service

In order to satisfy the needs of fast and convenient customization of manufacturing scientific data sharing service, the data service customization process and its key technologies were studied. First the data resource model and the customization oriented professional data service model were studied. Then the processes of service customization, from resource registration, service definition, service parsing, to service generating, were analyzed. The parsing engine based on service parsing technology and incubator based on service generating technology was emphasized. Finally the prototype system was developed and validated by an example.

scholarly journals Distributed Storage Strategy and Visual Analysis for Economic Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xiangli Chang ◽  
Hailang Cui
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Visual Analysis ◽  
Distributed Storage ◽  
Storage System ◽  
Service Level Agreement ◽  
Wiener Filter ◽  
Service Level ◽  
Prototype System ◽  
Data Types

With the increasing popularity of a large number of Internet-based services and a large number of services hosted on cloud platforms, a more powerful back-end storage system is needed to support these services. At present, it is very difficult or impossible to implement a distributed storage to meet all the above assumptions. Therefore, the focus of research is to limit different characteristics to design different distributed storage solutions to meet different usage scenarios. Economic big data should have the basic requirements of high storage efficiency and fast retrieval speed. The large number of small files and the diversity of file types make the storage and retrieval of economic big data face severe challenges. This paper is oriented to the application requirements of cross-modal analysis of economic big data. According to the source and characteristics of economic big data, the data types are analyzed and the database storage architecture and data storage structure of economic big data are designed. Taking into account the spatial, temporal, and semantic characteristics of economic big data, this paper proposes a unified coding method based on the spatiotemporal data multilevel division strategy combined with Geohash and Hilbert and spatiotemporal semantic constraints. A prototype system was constructed based on Mongo DB, and the performance of the multilevel partition algorithm proposed in this paper was verified by the prototype system based on the realization of data storage management functions. The Wiener distributed memory based on the principle of Wiener filter is used to store the workload of each workload distributed storage window in a distributed manner. For distributed storage workloads, this article adopts specific types of workloads. According to its periodicity, the workload is divided into distributed storage windows of specific duration. At the beginning of each distributed storage window, distributed storage is distributed to the next distributed storage window. Experiments and tests have verified the distributed storage strategy proposed in this article, which proves that the Wiener distributed storage solution can save platform resources and configuration costs while ensuring Service Level Agreement (SLA).

scholarly journals Research on Distributed Data Sharing System based on Internet of Things and Blockchain

2021 ◽  
Vol 9 (3) ◽  
pp. 239-254
Author(s):  
Enchang Sun ◽  
Kang Meng ◽  
Ruizhe Yang ◽  
Yanhua Zhang ◽  
Meng Li
Keyword(s):  
Internet Of Things ◽  
Data Sharing ◽  
Data Storage ◽  
Network Architecture ◽  
Data Privacy ◽  
Distributed Storage ◽  
Global Network ◽  
Local Network ◽  
Network Data ◽  
Distributed Data

Abstract Aiming at the problems of the traditional centralized data sharing platform, such as poor data privacy protection ability, insufficient scalability of the system and poor interaction ability, this paper proposes a distributed data sharing system architecture based on the Internet of Things and blockchain technology. In this system, the distributed consensus mechanism of blockchain and the distributed storage technology are employed to manage the access and storage of Internet of Things data in a secure manner. Up to the physical topology of the network, a hierarchical blockchain network architecture is proposed for local network data storage and global network data sharing, which reduces networking complexity and improves the scalability of the system. In addition, smart contract and distributed machine learning are adopted to design automatic processing functions for different types of data (public or private) and supervise the data sharing process, improving both the security and interactive ability of the system.

Ontology-Based Design Rationale Retrieval Supporting Natural Language Query

2014 ◽  
Author(s):  
Luye Li ◽  
Feiwei Qin ◽  
Shuming Gao ◽  
Xiaolian Qin
Keyword(s):  
Natural Language ◽  
Template Matching ◽  
Sparql Query ◽  
Design Knowledge ◽  
Prototype System ◽  
Design Domain ◽  
Design Rationale ◽  
Generating Method ◽  
Natural Language Query ◽  
Successful Retrieval

Design rationale is an important category of design knowledge. Effective reuse of design rationale depends on its successful retrieval. In this paper, an ontology-based design rationale retrieval approach is presented, based on which, users can input natural language questions to search what they want. First, a database of ontology-based design rationale is constructed, which supports SPARQL query. Then, several SPARQL templates are defined according to different knowledge query requirements in engineering design domain. Moreover, a SPARQL query generating method is proposed to generate SPARQL query from natural language query automatically through template matching and keywords extending. Finally, a design rationale retrieval prototype system is implemented, and the experimental results show the advantages of the proposed approach.

scholarly journals A Microfluidic Prototype System towards Microalgae Cell Separation, Treatment and Viability Characterization

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4940
Author(s):  
Yanjuan Wang ◽  
Junsheng Wang ◽  
Chen Zhou ◽  
Gege Ding ◽  
Mengmeng Chen ◽  
...  
Keyword(s):  
Concentration Gradient ◽  
Cell Separation ◽  
Single Photon ◽  
Lateral Displacement ◽  
Microfluidic System ◽  
Prototype System ◽  
Separation Function ◽  
Fast Analysis ◽  
Gradient Generator ◽  
Detection Technology

There are a huge number, and abundant types, of microalgae in the ocean; and most of them have various values in many fields, such as food, medicine, energy, feed, etc. Therefore, how to identify and separation of microalgae cells quickly and effectively is a prerequisite for the microalgae research and utilization. Herein, we propose a microfluidic system that comprised microalgae cell separation, treatment and viability characterization. Specifically, the microfluidic separation function is based on the principle of deterministic lateral displacement (DLD), which can separate various microalgae species rapidly by their different sizes. Moreover, a concentration gradient generator is designed in this system to automatically produce gradient concentrations of chemical reagents to optimize the chemical treatment of samples. Finally, a single photon counter was used to evaluate the viability of treated microalgae based on laser-induced fluorescence from the intracellular chlorophyll of microalgae. To the best of our knowledge, this is the first laboratory prototype system combining DLD separation, concentration gradient generator and chlorophyll fluorescence detection technology for fast analysis and treatment of microalgae using marine samples. This study may inspire other novel applications of micro-analytical devices for utilization of microalgae resources, marine ecological environment protection and ship ballast water management.

scholarly journals Getting Started Creating Data Dictionaries: How to Create a Shareable Dataset

2019 ◽  
Author(s):  
Erin Michelle Buchanan ◽  
Sarah E Crain ◽  
Ari L. Cunningham ◽  
Hannah Rose Johnson ◽  
Hannah Elyse Stash ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Sharing ◽  
Search Engine ◽  
Search Engines ◽  
Web Applications ◽  
Data Dictionary ◽  
Entire Process ◽  
Source Data

As researchers embrace open and transparent data sharing, they will need to provide information about their data that effectively helps others understand its contents. Without proper documentation, data stored in online repositories such as OSF will often be rendered unfindable and unreadable by other researchers and indexing search engines. Data dictionaries and codebooks provide a wealth of information about variables, data collection, and other important facets of a dataset. This information, called metadata, provides key insights into how the data might be further used in research and facilitates search engine indexing to reach a broader audience of interested parties. This tutorial first explains the terminology and standards surrounding data dictionaries and codebooks. We then present a guided workflow of the entire process from source data (e.g., survey answers on Qualtrics) to an openly shared dataset accompanied by a data dictionary or codebook that follows an agreed-upon standard. Finally, we explain how to use freely available web applications to assist this process of ensuring that psychology data are findable, accessible, interoperable, and reusable (FAIR; Wilkinson et al., 2016).

scholarly journals A blockchain-assisted framework for secure and reliable data sharing in distributed systems

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yu Guo ◽  
Shenling Wang ◽  
Jianhui Huang
Keyword(s):  
Distributed Systems ◽  
Data Sharing ◽  
Keyword Search ◽  
Distributed Storage ◽  
Data Access ◽  
Third Party ◽  
Superior Performance ◽  
User Privacy ◽  
Local Index ◽  
Data Store

AbstractThe explosive growth of big data is pushing forward the paradigm of cloud-based data store today. Among other, distributed storage systems are widely adopted due to their superior performance and continuous availability. However, due to the potentially wide attacking surfaces of the public cloud, outsourcing data store inevitably raises new concerns on user privacy exposure and unauthorized data access. Besides, directly introducing a centralized third-party authority for query authorization management does not work because it still can be compromised. In this paper, we propose a blockchain-assisted framework that can support trustworthy data sharing services. In particular, data owners allow to outsource their sensitive data to distributed systems in encrypted form. By leveraging smart contracts of blockchain, a data owner can distribute secret keys for authorized users without extra round interaction to generate the permitted search tokens. Meanwhile, such blockchain-assisted framework naturally solves the trust issues of query authorization. Besides, we devise a secure local index framework to support encrypted keyword search with forward privacy and mitigate blockchain overhead. To validate our design, we implement the prototype and deploy it at Amazon Cloud. Extensive experiments demonstrate the security, efficiency, and effectiveness of the blockchain-assisted design.

scholarly journals Traceable Multiauthority Attribute-Based Encryption with Outsourced Decryption and Hidden Policy for CIoT

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Suhui Liu ◽  
Jiguo Yu ◽  
Chunqiang Hu ◽  
Mengmeng Li
Keyword(s):  
Data Sharing ◽  
Random Oracle Model ◽  
Random Oracle ◽  
Fine Grained ◽  
Security Issues ◽  
Efficient Management ◽  
Attribute Based Encryption ◽  
High Efficient ◽  
Efficient Data ◽  
Iot Devices

Cloud-assisted Internet of Things (IoT) significantly facilitate IoT devices to outsource their data for high efficient management. Unfortunately, some unsettled security issues dramatically impact the popularity of IoT, such as illegal access and key escrow problem. Traditional public-key encryption can be used to guarantees data confidentiality, while it cannot achieve efficient data sharing. The attribute-based encryption (ABE) is the most promising way to ensure data security and to realize one-to-many fine-grained data sharing simultaneously. However, it cannot be well applied in the cloud-assisted IoT due to the complexity of its decryption and the decryption key leakage problem. To prevent the abuse of decryption rights, we propose a multiauthority ABE scheme with white-box traceability in this paper. Moreover, our scheme greatly lightens the overhead on devices by outsourcing the most decryption work to the cloud server. Besides, fully hidden policy is implemented to protect the privacy of the access policy. Our scheme is proved to be selectively secure against replayable chosen ciphertext attack (RCCA) under the random oracle model. Some theory analysis and simulation are described in the end.

scholarly journals Enhanced SPARQL-based design rationale retrieval

2016 ◽  
Vol 30 (4) ◽  
pp. 406-423
Author(s):  
Luye Li ◽  
Shuming Gao ◽  
Ying Liu ◽  
Xiaolian Qin
Keyword(s):  
Natural Language ◽  
Template Matching ◽  
Sparql Query ◽  
Design Knowledge ◽  
Prototype System ◽  
Semantic Model ◽  
Design Rationale ◽  
Query Generation ◽  
Successful Retrieval ◽  
Issue Based Information System

AbstractDesign rationale (DR) is an important category within design knowledge, and effective reuse of it depends on its successful retrieval. In this paper, an ontology-based DR retrieval approach is presented, which allows users to search by entering normal queries such as questions in natural language. First, an ontology-based semantic model of DR is developed based on the extended issue-based information system-based DR representation in order to effectively utilize the semantics embedded in DR, and a database of ontology-based DR is constructed, which supports SPARQL queries. Second, two SPARQL query generation methods are proposed. The first method generates initial SPARQL queries from natural language queries automatically using template matching, and the other generates initial SPARQL queries automatically from DR record-based queries. In addition, keyword extension and optimization is conducted to enhance the SPARQL-based retrieval. Third, a design rationale retrieval prototype system is implemented. The experimental results show the advantages of the proposed approach.

Sign in / Sign up

Export Citation Format

Share Document