Query Functions Research Articles

SFB: a scalable method for handling range queries on Skip Graphs

Skip Graph is a promising candidate algorithm for large scale distributed systems. The principal feature is range query functionality, but Skip Graph does not have a definite method of multicasting inside ranges designated by query issuers. Even though several simple ways can be considered, they are inefficient regarding the latency or traffic volume. In this letter, we first introduce Multi-Range Forwarding (MRF) used in Multi-key Skip Graph. MRF can be used even in normal Skip Graph, and is efficient compared to the simple ways. Second, we propose a method named Split-Forward Broadcasting (SFB). We analytically evaluate SFB and explain that SFB can roughly halve the average number of hops of MRF.

An Intelligent Tool Selection System Based on CRB and RBR

The manufacturing industries dealing with machining and tools' manufacturing generate a large amount of data concerning the machining process. However, in most cases, this information is not used efficiently as there is no system dedicated for storing and manipulating such data. In order to use these data to improve the efficiency and increase the benefit of machining, the cutting database system is built for these manufacturing enterprises, which can provide rational cutting parameters and tool solutions. JAVA and MYSQL are used as the base of database management system, combining XML and JavaScript technology. The intelligent tool selection model was designed, and the functions of basic information management and query were realized. It was concluded that it can commend reasonable cutting parameters and tool solutions according to machining condition input.

A New Randomized Order Preserving Encryption Scheme

Order Preserving Encryption (OPE) schemes have been examined to a great extent in the cryptography literature because of their prospective application to database design. OPE is an appealing method for database encryption as it permits to execute sort and range queries in an efficient manner without decrypting the data. Databases such as CryptDB are beginning to employ encryption to guard sensitive data. No existing OPE schemes that were proposed in the literature achieved IND-OCPA security except mutable Order-Preserving Encoding (mOPE) scheme, the first OPE scheme that satisfies IND-OCPA with respect to OPE encodings. However, mOPE scheme uses DET (deterministic encryption) to encrypt the plaintext values which leads to leakage of distribution of plaintext domain. This paper proposes a scheme called as Randomized Order Preserving Encryption (ROPE), a novel OPE scheme that leaks nothing beyond the order. ROPE follows the mOPE scheme by contributing randomness to it, so as to accomplish INDOCPA security. The ROPE scheme implements insert, delete and query functions on an encrypted MySQL database. ROPE scheme permits various SQL queries to be employed instantly on encrypted data.The performance of ROPE scheme is compared with the existing DOPE scheme and observed that there is a query retrieval time overhead. Still, ROPE scheme renders more confidentiality and attains the IND-OCPA security for OPE when compared to the existing OPE schemes. General Terms Security, Algorithms

A sensor data mediator bridging the OGC Sensor Observation Service (SOS) and the OASIS Open Data Protocol (OData)

The World-Wide Sensor Web is generating tremendous amount of real-time sensor data streams and will enable scientists to observe phenomena that are previously unobservable. As the concept of sensor web is to connect all the sensors and their data to achieve shared goals, improving the openness and accessibility of sensor data is important. The Open Geospatial Consortium Sensor Observation Service (SOS) defines standard web service protocols for sharing sensor data online in an interoperable manner. However, SOS has a relatively weak ecosystem, which makes it difficult to build and consume, and it only supports predefined queries. On the other hand, the OASIS1 Open Data Protocol (OData) has a strong ecosystem and flexible query functions. But the soft-typing approach of OData requires it to have a commonly agreed data model to be interoperable. As we argue that the two standards can benefit from each other, we propose a sensor data mediator solution and define an SOS entity data model for OData (SOS-OData) to bridge these two standards. Our prototype demonstrates that the proposed system can convert between existing SOS and SOS-OData services. As a result, we not only can consume SOS data with the flexible OData query functions, but can also easily build an SOS-compliant service with the strong OData ecosystem. We argue that the bridging between these two standards would consequently lead us to the vision of open data for sensor web.

Design and Implementation of Portable Test Diagnosis Instrument for a Type of Electronic Equipment

To solve the problem of a type of electronic equipment maintenance, a kind of portable testing instrument is developed. The instrument adopts embedded development, PC104 bus and fault reasoning technology to realize the functions of signal test, fault diagnosis, repair instructions and data query. The instrument is mainly composed of a hand-held chassis, LCD screen, touch screen, Intel main control board, oscilloscope card, multimeter card and power supply module, on the basis of PC104 bus structure. The A/D conversion circuit uses multiple cross parallel high-speed data acquisition architecture to achieve sampling data of higher accuracy and higher speed. The main control board and test function card is designed and the principle of circuit module is described. The signal testing and fault diagnosis software is developed. The instrument has good application effect, with the advantage of practical and portable.

Design on Biochemical Indicators Management System for Athletes

Biochemical indicators have the role of monitoring for competitive sports level data, the use of biochemical indicators to assess the fitness and health status of athletes, the results is more accurate. In this paper, a system designed to provide technical support for the athletes and biochemical indicators of management systems development. First, choose a representative biochemical indicators, and indicators are described; then, processed the database logical structure design, including athlete information, sample information, biochemical markers, and other related fields; finally, functional design, including data entry, data modification, data query, data reporting, system management and other main function module. Using results of this study can give full play to the role of technology in competitive sports, to achieve scientific training.

Agricultural Information Remote Collection System Based on Zigbee Research

In order to realize the real-time monitoring of crop growth environment, this paper applied the Java JDK 6 + Eclipse 3.5, developed agricultural remote data collection system based on zigbee, greenhouse planting of crops growth environment parameters, such as air temperature, humidity and so on, with all kinds of sensor dynamic collection, and transmitted to the user through the network client terminal. This makes agricultural personnel through the terminal computer real time control of the growth environment of crops, can take timely control measures, has realized the remote, multiobjective agricultural information, the parameters of the real-time acquisition, display, storage, query, statistics, and other functions, provides a effective way for the agricultural informatization construction.

Dictionary Writing System (DWS) + Corpus Query Package (CQP): The Case of TshwaneLex

Abstract: In this article the integrated corpus query functionality of the dictionary compilation software TshwaneLex is analysed. Attention is given to the handling of both raw corpus data and annotated corpus data. With regard to the latter it is shown how, with a minimum of human effort, machine learning techniques can be employed to obtain part-of-speech tagged corpora that can be used for lexicographic purposes. All points are illustrated with data drawn from English and Northern Sotho. The tools and techniques themselves, however, are language-independent, and as such the encouraging outcomes of this study are far-reaching.

Modular design, application architecture, and usage of a self-service model for enterprise data delivery: The Duke Enterprise Data Unified Content Explorer (DEDUCE)

PurposeData generated in the care of patients are widely used to support clinical research and quality improvement, which has hastened the development of self-service query tools. User interface design for such tools, execution of query activity, and underlying application architecture have not been widely reported, and existing tools reflect a wide heterogeneity of methods and technical frameworks. We describe the design, application architecture, and use of a self-service model for enterprise data delivery within Duke Medicine. MethodsOur query platform, the Duke Enterprise Data Unified Content Explorer (DEDUCE), supports enhanced data exploration, cohort identification, and data extraction from our enterprise data warehouse (EDW) using a series of modular environments that interact with a central keystone module, Cohort Manager (CM). A data-driven application architecture is implemented through three components: an application data dictionary, the concept of “smart dimensions”, and dynamically-generated user interfaces. ResultsDEDUCE CM allows flexible hierarchies of EDW queries within a grid-like workspace. A cohort “join” functionality allows switching between filters based on criteria occurring within or across patient encounters. To date, 674 users have been trained and activated in DEDUCE, and logon activity shows a steady increase, with variability between months. A comparison of filter conditions and export criteria shows that these activities have different patterns of usage across subject areas. ConclusionsOrganizations with sophisticated EDWs may find that users benefit from development of advanced query functionality, complimentary to the user interfaces and infrastructure used in other well-published models. Driven by its EDW context, the DEDUCE application architecture was also designed to be responsive to source data and to allow modification through alterations in metadata rather than programming, allowing an agile response to source system changes.

A dedicated database system for handling multi-level data in systems biology.

BackgroundAdvances in high-throughput technologies have enabled extensive generation of multi-level omics data. These data are crucial for systems biology research, though they are complex, heterogeneous, highly dynamic, incomplete and distributed among public databases. This leads to difficulties in data accessibility and often results in errors when data are merged and integrated from varied resources. Therefore, integration and management of systems biological data remain very challenging.MethodsTo overcome this, we designed and developed a dedicated database system that can serve and solve the vital issues in data management and hereby facilitate data integration, modeling and analysis in systems biology within a sole database. In addition, a yeast data repository was implemented as an integrated database environment which is operated by the database system. Two applications were implemented to demonstrate extensibility and utilization of the system. Both illustrate how the user can access the database via the web query function and implemented scripts. These scripts are specific for two sample cases: 1) Detecting the pheromone pathway in protein interaction networks; and 2) Finding metabolic reactions regulated by Snf1 kinase.Results and conclusionIn this study we present the design of database system which offers an extensible environment to efficiently capture the majority of biological entities and relations encountered in systems biology. Critical functions and control processes were designed and implemented to ensure consistent, efficient, secure and reliable transactions. The two sample cases on the yeast integrated data clearly demonstrate the value of a sole database environment for systems biology research.

Study on the Design of Mobile Digital Library Based on 3G Mobile Internet Technology

We use hierarchical data mining to compress the mobile communication data, which improves data transmission rate of 3G mobile internet. Based on this, combining the advantages of VC and MATLAB, we design the digital library architecture of 3G mobile internet model. This model has the functions of inquiry, backstage management, and mobile data optimization and so on. In order to verify the effectiveness and reliability of the model, we use network testing of small digital library as an example to test the library query function with mobile phone. By using the Android test terminal we get the library query process curve and the data flow transmission characteristics. It provides technical support for 3G on mobile internet technology and innovative design of library.

Research on Agent-Based Mobile Database Structure

With the transaction processing, data query, location query and other functions of mobile devices, the demand for mobile database has also increased. The analysis of the agent-based mobile database structure and the deficiencies of existing systems provide the basis for future research..

A Batch-mode Active Learning Method Based on the Nearest Average-class Distance (NACD) for Multiclass Brain-Computer Interfaces

In this paper, a novel batch-mode active learning method based on the nearest average-class distance (ALNACD) is proposed to solve multi-class problems with Linear Discriminate Analysis (LDA) classiflers. Using the Nearest Average-class Distance (NACD) query function, the ALNACD algorithm selects a batch of most uncertain samples from unlabeled data to improve gradually pre-trained classiflers’ performance. As our method only needs a small set of labeled samples to train initial classiflers, it is very useful in applications like Brain-computer Interface (BCI) design. To verify the efiectiveness of the proposed ALNACD method, we test the ALNACD algorithm on the Dataset 2a of BCI Competition IV. The test results show that the ALNACD algorithm ofiers similar classiflcation results using less sample labeling efiort than Random Sampling (RS) method. It also provides competitive results compared with active Support Vector Machine (active SVM), but uses less time than the active SVM in terms of the training.

Optimization on Hardware Inventory of the Steel Pipe Enterprise Based on Cluster-Algorithm

Based on the existing data accumulation and process of stock management of a Steel pipe enterprise, a general analysis for its current inventory structure is done, then a classification research for hardware inventory is done with the theory of cluster-algorithm. Finally, a visualizing stock query function which is the main contribution of this paper is developed based on the research above.

Active Learning in the Spatial Domain for Remote Sensing Image Classification

Active learning (AL) algorithms have been proven useful in reducing the number of required training samples for remote sensing applications; however, most methods query samples pointwise without considering spatial constraints on their distribution. This may often lead to a spatially dispersed distribution of training points unfavorable for visual image interpretation or field surveys. The aim of this study is to develop region-based AL heuristics to guide user attention toward a limited number of compact spatial batches rather than distributed points. The proposed query functions are based on a tree ensemble classifier and combine criteria of sample uncertainty and diversity to select regions of interest. Class imbalance, which is inherent to many remote sensing applications, is addressed through stratified bootstrap sampling. Empirical tests of the proposed methods are performed with multitemporal and multisensor satellite images capturing, in particular, sites recently affected by large-scale landslide events. The assessment includes an experimental evaluation of the labeling time required by the user and the computational runtime, and a sensitivity analysis of the main algorithm parameters. Region-based heuristics that consider sample uncertainty and diversity are found to outperform pointwise sampling and region-based methods that consider only uncertainty. Reference landslide inventories from five different experts enable a detailed assessment of the spatial distribution of remaining errors and the uncertainty of the reference data.

YouFlow Microblog

Purpose – To present YouFlow Microblog and how its functionalities for discourse structuring and message classification allows improving learning. The paper aims to discuss these issues. Design/methodology/approach – The authors developed a survey on microblogs and its functionalities for supporting education, and then the authors developed a new microblog called YouFlow Microblog, which was used in a case study to illustrate its applicability. Findings – The results were: the categorization of messages according to the lesson plan allowed deeper discussions; the amount of messages and preference of the students for the categorization approach are directly related; the filtering of messages (categorization on the reading time) was used as a facilitator of the discussion understanding; the students prefer the search results organized by the categories and related messages; and the students were able to follow the discussions supported by the tree structure of discourse, categorization of messages according to the lesson plan and searches or filtering of messages. Research limitations/implications – Other case studies involving more students and from different knowledge areas could be performed. Other approaches for messages' visualization for reducing the amount of information could be explored, such as recommendation of messages in microblogs; and groupings of messages according to date, priority and subject. Practical implications – The developed microblog is useful and proved to be interesting for applying in the educational context. Originality/value – No other microblog allows different structuring of messages and the their classification according to the lesson plan, in addition to filtering and query functionalities.

A Generalized Model of PAC Learning and its Applicability

We combine a new data model, where the random classification is subjected to rather weak restrictions which in turn are based on the Mammen−Tsybakov [E. Mammen and A.B. Tsybakov, Ann. Statis. 27 (1999) 1808–1829; A.B. Tsybakov, Ann. Statis. 32 (2004) 135–166.] small margin conditions, and the statistical query (SQ) model due to Kearns [M.J. Kearns, J. ACM 45 (1998) 983–1006] to what we refer to as PAC + SQ model. We generalize the class conditional constant noise (CCCN) model introduced by Decatur [S.E. Decatur, in ICML ’97: Proc. of the Fourteenth Int. Conf. on Machine Learn. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA (1997) 83–91] to the noise model orthogonal to a set of query functions. We show that every polynomial time PAC + SQ learning algorithm can be efficiently simulated provided that the random noise rate is orthogonal to the query functions used by the algorithm given the target concept. Furthermore, we extend the constant-partition classification noise (CPCN) model due to Decatur [S.E. Decatur, in ICML ’97: Proc. of the Fourteenth Int. Conf. on Machine Learn. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA (1997) 83–91] to what we call the constant-partition piecewise orthogonal (CPPO) noise model. We show how statistical queries can be simulated in the CPPO scenario, given the partition is known to the learner. We show how to practically use PAC + SQ simulators in the noise model orthogonal to the query space by presenting two examples from bioinformatics and software engineering. This way, we demonstrate that our new noise model is realistic.

A Neural Approach Under Active Learning Mode for Change Detection in Remotely Sensed Images

In this paper, a change detection technique using neural networks in active learning framework is proposed under the scarcity of labeled patterns. In the present investigation, two variants of radial basis function neural networks and a multilayer perceptron are used as learners. Instead of training the network (or ensemble of networks) with randomly collected labeled patterns, in the proposed work, the network (or ensemble of networks) is iteratively trained with label patterns, collected using the query functions. Here, two query selection strategies are used: uncertainty sampling and query-by-committee. In this way, the most informative set of labeled patterns can be iteratively generated by querying. To evaluate the effectiveness of the proposed approach, the experiments are conducted on multi-temporal remotely sensed images. The results obtained using the proposed active learning framework are found to be encouraging.

The research and implementation of coalfield spontaneous combustion of carbon emission WebGIS based on Silverlight and ArcGIS server

As an important sub-topic of the natural process of carbon emission data public information platform construction, coalfield spontaneous combustion of carbon emission WebGIS system has become an important study object. In connection with data features of coalfield spontaneous combustion carbon emissions (i.e. a wide range of data, which is rich and complex) and the geospatial characteristics, data is divided into attribute data and spatial data. Based on full analysis of the data, completed the detailed design of the Oracle database and stored on the Oracle database. Through Silverlight rich client technology and the expansion of WCF services, achieved the attribute data of web dynamic query, retrieval, statistical, analysis and other functions. For spatial data, we take advantage of ArcGIS Server and Silverlight-based API to invoke GIS server background published map services, GP services, Image services and other services, implemented coalfield spontaneous combustion of remote sensing image data and web map data display, data analysis, thematic map production. The study found that the Silverlight technology, based on rich client and object-oriented framework for WCF service, can efficiently constructed a WebGIS system. And then, combined with ArcGIS Silverlight API to achieve interactive query attribute data and spatial data of coalfield spontaneous emmission, can greatly improve the performance of WebGIS system. At the same time, it provided a strong guarantee for the construction of public information on China's carbon emission data.

The Key Technology for Mobile Digital Campus

With the widespread use of mobile devices, universities are speeding up the construction of digital campus. Mobile digital campus system described in this paper contains campus view, voice campus navigation, voice dialogue, information query, campus news, books query, study BBS and other functions. Its key technology combines the query with voice, maps and other features forms an integrated Android mobile application platform with powerful function and program scalability.