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الملخص: أهداف البحث: تدار المستشفيات المحلية من قبل جمعية المرضى المستقيلة كقنوات اتصال سلبية. بيانات المستشفى عبر الإنترنت المتعلقة باستفسارات المستخدمين ليست مضمونة من حيث الشفافية والموثوقية. لذلك، من الضروري أن يكون لديك روبوت محادثة ذكي على الويب يمكنه استكشاف إدارة طلبات المستخدم، وتوفير وصول سريع لمعلومات المستشفى المحلي. في هذه الورقة، قدم المؤلفون إطار عمل ووظيفة روبوت محادثة تم تطويره بواسطة خبراء في تقنيات الويب. طرق البحث: تم دمج محرك الروبوت من خلال العديد من مناهج التعلم الآلي مثل خوارزميات الانحدار ومعالجة اللغة الطبيعية. وتم تقسيم البيانات المدربة التي تم إدخالها في الروبوت إلى مجموعات من الكلمات المصغرة وتم تطبيق خوارزمية الانحدار على كل دفعة صغيرة بالتتابع. طرق البرمجة اللغوية العصبية المتضمنة في تحويل الكلمة إلى جذعها مع نتيجة نصية منخفضة يمكن قراءتها من قبل البشر. النتائج: تم دمج الخوارزميات المستخدمة بنجاح لإدارة المزامنة البديلة للرسائل النصية والصوتية. الاستنتاجات: يمكن أن يكون الروبوت المقترح حلاً أفضل لاستخراج البيانات من المستشفيات المحلية. إنها قناة اتصال ثاقبة لكل من المستخدمين وموظفي المستشفى. Abstract: Objectives: Local hospitals are operated by the resigned association of patients as passive communication channels. The online hospital data related to the users’ queries are not transparent and reliable. Therefore, it is crucial to have an intelligent web chatbot that manages user requests and provides quick access to local hospital information. In this paper, we present a framework and functionality of a chatbot developed using web technologies. Methods: The bot engine was integrated by several machine learning approaches like gradient descent (GD) and natural language processing (NLP) algorithms. The trained data entered into the bot were split into mini-word batches, and the GD algorithm was applied sequentially on each mini-batch. The NLP methods involved in converting a word to its stem with a text result less readable by humans. Results: The employed ML algorithms were successfully incorporated to manage the alternative synchronisation of text and voice messages. Conclusions: The proposed bot can be a better solution for data extraction from local hospital which functioning as a good communication channel for both users and hospital staff and helpful in reducing the crowd.

SQL (Structured Query Language) is a structured language for specialized purposes used to communicate with the data stored in a database management system. It uses dynamic and sophisticated query commands for processing and controlling data in a database, which can become an obstacle for users with no previous experience. In order to address this constraint, we have analyzed the existing models in Natural Language Processing, which convert a native-language query into an SQL query. Thus, any novice user can use the SQL program and eliminate the need to generate any complex queries. This work is a detailed survey of the existing literature.

This research was conducted to measure the data storage time carried out by the DBMS on data that has been prepared with an increasing number of data, the data provided is consistent student data which will be stored with Stored Procedure and Function. This study uses the action research method which has 4 stages, starting with planning, action, observation and reflection. From the results of the experiments that have been carried out, it appears that Stored Procedure is able to outperform Function in data storage time. The data provided are 2 different types of data, each of which consists of 500, 2500, 4500 and 6500 data stages. This study also compares data storage that is differentiated by the computer between the data provider computer and the data storage computer or server, the result of which is the Stored Procedure. able to outperform Function in data storage speed.

Dramatically increasing collection of point clouds raises an essential demand for highly efficient data management. It can also facilitate modern applications such as robotics and virtual reality. Extensive studies have been performed on point data management and querying, but most of them concentrate on low dimensional spaces. High dimensional data management solutions from computer science have not considered the special features of spatial data; so, they may not be optimal. A Space Filling Curve (SFC) based approach, PlainSFC which is capable of nD point querying has been proposed and tested in low dimensional spaces. However, its efficiency in nD space is still unknown. Besides that, PlainSFC performs poorly on skewed data querying. This paper develops HistSFC which utilizes point distribution information to improve the querying efficiency on skewed data. Then, the paper presents statistical analysis of how PlainSFC and HistSFC perform when dimensionality increases. By experimenting on simulated nD data and real data, we confirmed the patterns deduced: for inhomogeneous data querying, the false positive rate (FPR) of PlainSFC increases drastically as dimensionality goes up. HistSFC alleviates such deterioration to a large extent. Despite performance degeneration in ultra high dimensional spaces, HistSFC can be applied with high efficiency for most spatial applications. The generic theoretical framework developed also allows us to study related topics such as visualization and data transmission in the future.

Document stores have become one of the key NoSQL storage solutions. They have been widely adopted in different domains due to their ability to store semi-structured data and expressive query capabilities. However, implementations differ in terms of concrete data storage and retrieval. Unfortunately, a standard framework for data and query optimization for document stores is nonexistent, and only implementation-specific design and query guidelines are used. Hence, the goal of this work is to aid automating the data design for document stores based on query costs instead of generic design rules. For this, we define a generic storage and query cost model based on disk access and memory allocation that allows estimating the impact of design decisions. Since all document stores carry out data operations in memory, we first estimate the memory usage by considering characteristics of the stored documents, their access patterns, and memory management algorithms. Then, using this estimation and metadata storage size, we introduce a cost model for random access queries. We validate our work on two well-known document store implementations: MongoDB and Couchbase. The results show that the memory usage estimates have the average precision of 91% and predicted costs are highly correlated to the actual execution times. During this work, we have managed to suggest several improvements to document storage systems. Thus, this cost model also contributes to identifying discordance between document store implementations and their theoretical expectations., SCOPUS: ar.j, info:eu-repo/semantics/published

Most of today's web applications run on relational database systems. Communication with them is possible through statements written in Structured Query Language (SQL). This paper presents the most popular relational database management systems and describes common ways to optimize SQL queries. Using the research environment based on fragment of the imdb.com database, implementing OracleDb, MySQL, Microsoft SQL Server and PostgreSQL engines, a number of test scenarios were performed. The aim was to check the performance changes of SQL queries resulting from syntax modication while maintaining the result, the impact of database organization, indexing and advanced mechanisms aimed at increasing the eciency of operations performed, delivered in the systems used. The tests were carried out using a proprietary application written in Java using the Hibernate framework.

This article discusses the idea of conducting a multistage process of building a search image of a query in natural language for use in the semantic search system. Modern methods and tools for processing natural language are widely used in the field of machine translation. Research on search engines and semantic search mainly focuses on data storage and further analysis. Most search engines use a huge amount of previously accumulated user queries to predict search results, without taking into account the user’s intention through quality query processing. The proposed approach is based on the selection of the maximum amount of information from the original request by means of syntactic and semantic analysis, as well as the use of synonymous extension techniques. This article describes the first step in the process of building a search query model, based on extracting syntactic dependencies from the original sentence.

This paper describes how knowledge bases can be represented in and reasoned with in natural logic. Natural logic is a regimented fragment of natural language possessing a well-defined logical semantics. As such, natural logic may be considered an attractive alternative among the various knowledge representation logics such as description logics. Our version of natural logic expands formal ontologies with affirmative propositions expressing a variety of relationships between concepts. It comprises (nested) restrictive relative clauses and prepositional phrases and, as a new construct, adverbial prepositional phrases. The natural logic knowledge base is to be used for deductive query answering applying inference rules. This is facilitated by introduction of Datalog as an embedding meta-logic. The inference rules are stated in Datalog and act directly on the natural logic formulations. The knowledge base propositions are decomposed into a graph form enabling path finding between concepts. The examples in the paper are derived from text source life-science descriptions.

Massive volumes of uncertain trajectory data are being generated by GPS devices. Due to the limitations of GPS data, these trajectories are generally uncertain. This state of affairs renders it is attractive to be able to compress uncertain trajectories and to be able to query the trajectories efficiently without the need for (full) decompression. Unlike existing studies that target accurate trajectories, we propose a framework that accommodates uncertain trajectories in road networks. To address the large cardinality of instances of a single uncertain trajectory, we exploit the similarity between uncertain trajectory instances and provide a referential representation. First, we propose a reference selection algorithm based on the notion of Fine-grained Jaccard Distance to efficiently select trajectory instances as references. Then we provide referential representations of the different types of information contained in trajectories to achieve high compression ratios. In particular, a new compression scheme for temporal information is presented to take into account variations in sample intervals. Finally, we propose an index and develop filtering techniques to support efficient queries over compressed uncertain trajectories. Extensive experiments with real-life datasets offer insight into the properties of the framework and suggest that it is capable of outperforming the existing state-of-the-art method in terms of both compression ratio and efficiency.

Topological relationships between spatio-temporal objects are the most fundamental elements in spatio-temporal database systems, GIS, and image database systems. The research issue of modeling topological relationships has increasingly attracted attention, especially for querying of spatio-temporal objects and reasoning of topological relationships. Currently, topological relationship operating on spatio-temporal objects with precisely defined boundaries has been well studied. However, in the real world, spatio-temporal objects are not always crisp but with the nature of fuzziness and imprecision. Therefore, how to model topological relationship between fuzzy spatio-temporal objects is a significant topic and needs more investigations. This paper presents a study on modeling topological relationships between fuzzy spatio-temporal objects. Firstly, we give a model of fuzzy spatio-temporal objects in three-dimensional space and define those objects as moving fuzzy points , moving fuzzy lines , and moving fuzzy regions . On this basis, we propose a model for identifying basic topological relations between fuzzy spatio-temporal objects. Furthermore, in order to describe the evolution of basic topological relations over time, we give a model of complex topological relationships which are the sequences of basic relationships. The benefit of this model is that the complex topological relationships can be used as fuzzy spatio-temporal query operators in query languages. Finally, we provide some query examples to demonstrate fuzzy spatio-temporal queries in spatio-temporal database.

As IoT grows at a staggering pace, the need for contextual intelligence is a fundamental and critical factor for IoT intelligence, efficiency, effectiveness, performance, and sustainability. As the standardisation efforts for IoT are fast progressing, efforts in standardising context management platforms led by the European Telecommunications Standards Institute (ETSI) are gaining more attention from both academic and industrial research organizations. These standardisation endeavours will enable intelligent interactions between &lsquo, things&rsquo, where things could be devices, software components, web-services, or sensing/actuating systems. Therefore, having a generic platform to describe and query context is crucial for the future of IoT applications. In this paper, we propose Context Definition and Query Language (CDQL), an advanced approach that enables things to exchange, reuse and share context between each other. CDQL consists of two main parts, namely: context definition model, which is designed to describe situations and high-level context, and Context Query Language (CQL), which is a powerful and flexible query language to express contextual information requirements without considering details of the underlying data structures. An important feature of the proposed query language is its ability to query entities in IoT environments based on their situation in a fully dynamic manner where users can define situations and context entities as part of the query. We exemplify the usage of CDQL on three different smart city use cases to highlight how CDQL can be utilised to deliver contextual information to IoT applications. Performance evaluation has demonstrated scalability and efficiency of CDQL in handling a fairly large number of concurrent context queries.

Simulation has become an indispensable technique for modelling and evaluating the performance of large-scale systems efficiently and at a relatively low cost. ElasticSearch (ES) is one of the most popular open source large-scale distributed data indexing systems worldwide. In this paper, we use the RECAP Discrete Event Simulator (DES) simulator, an extension of CloudSimPlus, to model and evaluate the performance of a real-world cloud-based ES deployment by an Irish small and medium-sized enterprise (SME), Opening.io. Following simulation experiments that explored how much query traffic the existing Opening.io architecture could cater for before performance degradation, a revised architecture was proposed, adding a new virtual machine in order to dissolve the bottleneck. The simulation results suggest that the proposed improved architecture can handle significantly larger query traffic (about 71% more) than the current architecture used by Opening.io. The results also suggest that the RECAP DES simulator is suitable for simulating ES systems and can help companies to understand their infrastructure bottlenecks under various traffic scenarios and inform optimisation and scalability decisions.

In recent years, materialized views (MVs) are widely used to enhance the database performance by storing pre-calculated results of resource-intensive queries in the physical memory. In order to identify which queries may be potentially materialized, database transaction log for a long period of time should be analyzed. The goal of analysis is to distinguish resource-intensive and frequently used queries collected from database log, and optimize these queries by implementation of MVs. In order to achieve greater efficiency of MVs, they were used not only for the optimization of single queries, but also for entire groups of queries that are similar in syntax and execution results. Thus, the problem stated in this article is the development of approach that will allow forming groups of queries with similar syntax around the most resource-intensive queries in order to identify the list of potential candidates for materialization. For solving this problem, we have applied the algorithm of categorical data clustering to the query grouping problem on the step of database log analysis and searching candidates for materialization. In the current work CLOPE algorithm was modified to cover the introduced problem. Statistical and timing indicators were taken into account in order to form the clusters around the most resource intensive queries. Application of modified algorithm CLOPE allowed to decrease calculable complexity of clustering and to enhance the quality of formed groups. ACM CCS (2012) Classification : Information systems→ Data management systems → Database management system engines→Query optimization; Computing methodologies→Cluster analysis *To cite this article: K. Novokhatska and O. Kungurtsev, "Application of Clustering Algorithm CLOPE to the Query Grouping Problem in the Field of Materialized View Maintenance", CIT. Journal of Computing and Information Technology , vol. 24, no. 1, pp. 79-89, 2016.

Wireless Sensor Networks (WSNs) are attracting attention from the research community. One of the key issues is to provide them with privacy protection. In recent years, a huge amount of contributions has been focused on this area. Surveys and literature reviews have also been produced to give a systematic view of the different approaches taken. However, no previous work has focused on privacy models, that is, the set of assumptions made to build the approach. In particular, this paper focuses on this matter by studying 41 papers of the last 5 years. We highlight the great differences appearing among related papers that could make them incompatible to be applied simultaneously. We propose a set of guidelines to build comprehensive privacy models so as to foster their comparability and suitability analysis for different scenarios. This work was supported by the MINECO Grant TIN2013-46469-R (Security and Privacy in the Internet of You (SPINY)) and the CAM Grant S2013/ICE-3095 (Cybersecurity,Data, and Risks (CIBERDINE)), which is cofunded by EuropeanFunds (FEDER). Furthermore, J.M. de Fuentes and L. González-Manzano were also partially supported by the Programa de Ayudas a la Movilidad of Carlos III University of Madrid.

Modern analytics applications use a diverse mix of libraries and functions. Unfortunately, there is no optimization across these libraries, resulting in performance penalties as high as an order of magnitude in many applications. To address this problem, we proposed Weld, a common runtime for existing data analytics libraries that performs key physical optimizations such as pipelining under existing, imperative library APIs. In this work, we further develop the Weld vision by designing an automatic adaptive optimizer for Weld applications, and evaluating its impact on realistic data science workloads. Our optimizer eliminates multiple forms of overhead that arise when composing imperative libraries like Pandas and NumPy, and uses lightweight measurements to make data-dependent decisions at run-time in ad-hoc workloads where no statistics are available, with sub-second overhead. We also evaluate which optimizations have the largest impact in practice and whether Weld can be integrated into libraries incrementally. Our results are promising: using our optimizer, Weld accelerates data science workloads by up to 23X on one thread and 80X on eight threads, and its adaptive optimizations provide up to a 3.75X speedup over rule-based optimization. Moreover, Weld provides benefits if even just 4--5 operators in a library are ported to use it. Our results show that common runtime designs like Weld may be a viable approach to accelerate analytics.

Requirements engineering plays a crucial role in the development process of an information system as it aims at providing a complete and accurate requirement specification. In the life cycle of a Data Repository (\({\mathcal {D}}{\mathcal {R}}\)) such as a database or a data warehouse, the requirements are mainly used to define the conceptual model once they have been identified from the informal specification. In this paper, we study the interest of requirements in the other phases of the \({\mathcal {D}}{\mathcal {R}}\) life cycle. As the data integration problem, handled in the Extract, Transform, Load (ETL) phase, comes from the heterogeneity of requirements, we introduce a requirement integration framework based on ontologies and a generic model to unify the used vocabularies and requirement languages. Then we propose an approach to check the consistency of the requirements, w.r.t. the integrity constraints defined on the logical schema using the formal B method. We also show that requirements help define appropriate access structures such as indexes and materialized views to optimize SQL queries of a \({\mathcal {D}}{\mathcal {R}}\). Our approach is based on transformation rules that identify important queries that will be executed on a \({\mathcal {D}}{\mathcal {R}}\) directly from the requirements. The experiments conducted on the Star Schema Benchmark (SSB) confirm the interest of this approach for the selection of different optimization structures. Finally, we present the OntoReqTool that implements the previous functionality on top of the OntoDB/OntoQL platform.

The amount of collected Earth Observation (EO) data is increasing immensely with a rate of several Terabytes of data per day. Simultaneously with this increasing of data, new trends for exploration and information retrieval are highly needed. In the last year, the proposed method tries to explore the EO data using Image Information Mining (IIM) approach in which primitive feature extraction and classification are the main steps, developing a new process chain and a new taxonomy for the retrieved categories, mainly based on human interaction, can be a good solution. This paper proposes to explore the content of images and to identify the number of objects and land cover categories that can be retrieved from high resolution TerraSAR-X data. We need to mention that is for the first time when for remote sensing a large data set (e.g., TerraSAR-X images) covering different cities over the world is annotated and for each category a taxonomy for high resolution data is defined. Applications that may result from this study can be a semantic catalogue for TerraSAR-X, urban crisis, disasters, etc. First, we strongly need an automatic or semi-automatic searching tool capable to find in large EO data set similar sub-images (i.e. patches) and to group them in categories. Secondary, we need to define a taxonomy that can be used to semantically annotate each category using the human interaction. The data set consists of 109 scenes that cover different areas over the world: 5 scenes from Africa, 27 scenes from Asia, 44 scenes from Europe, 11 scenes from Middle East, and 22 scenes from North and South of America. These scenes are grouped in collections based on three criteria in order: (1) to get an idea about how many categories can be retrieved for each city/country/continent, (2) to see whether the same urban categories belong to two different scenes, and (3) to help us to annotate large data. Data set is grouped in more collections using previous three criteria and each collection is process separately using an enhanced methodology that take the scene/scenes and tile them into patches. Gabor filters is used as a primitive features method and is applied to each patch. Support vector machine with relevance feedback is implemented in order to group the features in categories of relevance. Finally, these categories are semantically annotated using as ground truth the Google Earth. In our investigation more than 850 categories were retrieved with their specific taxonomy. The novelty of this paper lies in the fact that this is the first time when a semantic annotation was made on a large number of scenes containing high resolution synthetic aperture radar images. This investigation has an important impact in applications such as classification of urban areas, infrastructure (e.g., airport, port, etc.), geography images (e.g., mountains, etc.), industrial sites, military sites, vegetation, and agriculture. The proposed taxonomies can be the basis for building a semantic catalogue for EO images. Finally, four types of query are defined and are planned to be integrated into the new system developed at DLR. The query provides opportunities to EO users to search into the database for some specific parameters or semantic of the existing data set.

A necessary condition of any information system’s efficient operation is to ensure that response time to user requests satisfies the subject area requirements. Actual is the problem of automated methods for choosing the tools reducing the execution time of queries to the database. Formal description of the system’s state and development gives the possibility to apply the most effective ways of speeding up queries in time when there is a need to improve system performance. While researching some dependences are obtained, allowing to predict the intensity of requests entering the information system, to determine the trend of system’s loading, to identify peak periods and set of the most commonly used tables and fields. The results on system operative features researching provide the possibility to predict system’s behavior and to take timely measures to maintain the required level of performance.

It is difficult for software professionals to find all the architectural knowledge they need from architecture documentation, and this results in wasted time and mistakes in projects.This is the case even when architecture documentation is indexed by an ontology and stored in a semantic wiki.We present a prototype tool called AK-Finder which queries architectural knowledge stored in a semantic wiki as Linked Open Data using a SPARQL endpoint.Our tool retrieves knowledge from the semantic wiki and answers questions for architectural review, design, and development activities.The tool exemplifies how systems used in the software project lifecycle can integrate and improve access to architectural knowledge in a semantic wiki.

Several schemes for k-nearest neighbors (k-NN) query over encrypted data in cloud have been proposed recently. Nevertheless, existing schemes either suppose each query user is fully-trusted, or need data owner to be online for each query. A fully-trusted query user is assumed to obtain the decryption key of data owner's outsourced dataset, thus, cloud server could entirely break the outsourced dataset upon gaining the decryption key from some untrustworthy query user. Because of the online requirement, data owner still needs to burden too many computational tasks during the k-NN queries, which thus is impractical. In this paper, we propose a new scheme to perform k-NN query over encrypted data in cloud while protecting the privacy of both data owner and query users from cloud. Our new method just reveals limited information about data owner's key to query users, and has no need of an online data owner. For gaining the properties, we present a new scalar product protocol, then the new protocol and some other transformation approaches are merged into our secure k-NN query system. Additionally, we confirm our security and efficiency through theoretical analysis and extensive simulation experiments.

A spatio-temporal database manages spatio-temporal objects and supports corresponding query languages. Today, the term moving objects databases is used as a synonym for spatio-temporal databases managing spatial objects with a continuously changing geospatial location and/or extent. Recent advances in wireless communication, miniaturization of spatially enabled devices and global navigation satellite systems (GNSS) services have resulted in a large number of novel application domains. Applications in these novel domains (geo-sensor networks, moving objects tracking, real-time traffic analysis, etc.) process huge volumes of continuous data streams, i.e. data sets that are produced incrementally over time, rather than those available in full before the processing begins. Several data stream management systems (DSMSs) have been developed to manage this data. Since they are mainly based on a relational paradigm, they do not support geospatial data. Therefore, there is an urgent need for geospatial data stream management, ranging from real-time monitoring and alerting to long-term analysis of processed geospatial data. In this paper we present a formal framework consisting of data types and operations needed to support geospatial data in data streams. It can be used as a basis either for implementation of a completely new geospatial DSMS, or for extending available open source products and research prototypes. We leverage the work on abstract data types from spatio-temporal databases, present an implementation based on user-defined aggregate functions and illustrate embedding into an SQL-like language.

Background: As suggested as early as in 2006, logs of queries submitted to search engines seeking information could be a source for detection of emerging influenza epidemics if changes in the volume of search queries are monitored (infodemiology). However, selecting queries that are most likely to be associated with influenza epidemics is a particular challenge when it comes to generating better predictions. Objective: In this study, we describe a methodological extension for detecting influenza outbreaks using search query data; we provide a new approach for query selection through the exploration of contextual information gleaned from social media data. Additionally, we evaluate whether it is possible to use these queries for monitoring and predicting influenza epidemics in South Korea. Methods: Our study was based on freely available weekly influenza incidence data and query data originating from the search engine on the Korean website Daum between April 3, 2011 and April 5, 2014. To select queries related to influenza epidemics, several approaches were applied: (1) exploring influenza-related words in social media data, (2) identifying the chief concerns related to influenza, and (3) using Web query recommendations. Optimal feature selection by least absolute shrinkage and selection operator (Lasso) and support vector machine for regression (SVR) were used to construct a model predicting influenza epidemics. Results: In total, 146 queries related to influenza were generated through our initial query selection approach. A considerable proportion of optimal features for final models were derived from queries with reference to the social media data. The SVR model performed well: the prediction values were highly correlated with the recent observed influenza-like illness (r=.956; P

As the fuzzy data management has become one of the main research topics and directions, the question of how to obtain the useful information by means of keyword query from fuzzy XML documents is becoming a subject of an increasing needed investigation. Considering the keyword query methods on crisp XML documents, smallest lowest common ancestor (SLCA) semantics is one of the most widely accepted semantics. When users propose the keyword query on fuzzy XML documents with the SLCA semantics, the query results are always incomplate, with low precision, and with no possibilities values returned. Most of keyword query semantics on XML documents only consider query results matching all keywords, yet users may also be interested in the query results matching partial keywords. To overcome these limitations, in this paper, we investigate how to obtain more comprehensive and meaningful results of keyword querying on fuzzy XML documents. We propose a semantics of object-oriented keyword querying on fuzzy XML documents. First, we introduce the concept of "object tree", analyze different types of matching result object trees and find the "minimum result object trees" which contain all keywords and "result object trees" which contain partial keywords. Then an object-oriented keyword query algorithm ROstack is proposed to obtain the root nodes of these matching result object trees, together with their possibilities. At last, experiments are conducted to verify the effectiveness and efficiency of our proposed algorithm.

Performance is an imperative aspect of a web-based application. Response time is one of the most important performance parameters. Most of web-based applications are driven by database system. Reducing response time of its database system will improve the performance of a web-based application. Caching is one of techniques that can be implemented to reduce response time. The implementation of caching can be conducted in the application or database side. Caching is a technique to keep the executed query and hence the query will not be executed when called in the later execution. The result of an executed query will be stored in the cache therefore when that query is called, the database will retrieve the stored result without re-executing query. This mechanism will reduce the time to re-execute queries. However, this technique may be not suitable for all queries. This research is intended to investigate the effect of the implementation of caching into performance of a database. A series of experiments have been conducted by applying query cache. The response time of the application of query cache is compared with the response time of database without query. The results show that the implementation of query cache reduces response time significantly. However, this technique is more suitable for large database with many frequently asked queries. The number of clients accessing the database is found to be influencing the performance of caching. The more clients access the database, the more improvement is provided by query cache.

In this paper, we present an information retrieval system based on the concept of fuzzy logic to relate vague and uncertain objects with un-sharp boundaries. The simple but comprehensive user interface of the system permits the entering of uncertain specifications in query forms. The system was modelled and simulated in a Matlab environment; its implementation was carried out using Borland C++ Builder. The result of the performance measure of the system using precision and recall rates is encouraging. Similarly, the smaller amount of more precise information retrieved by the system will positively impact the response time perceived by the users.

A new kind of metadata offers a synthesized view of an attribute’s values for a user to exploit when creating or refining a search query in data integration systems. The extraction technique that obtains these values is automatic and independent of an attribute domain but parameterized with various metrics for similarity measures. The authors describe a fully implemented prototype and some experimental results to show the effectiveness of “relevant values” when searching a knowledge base.

This study uses Twitter data to provide a more nuanced understanding of the public reaction to the 2011 reform to the propane gas subsidy in El Salvador. By soliciting a small sample of manually tagged tweets, the study identifies the subject matter and sentiment of all tweets during six one-month periods over three years that concern the subsidy reform. The paper shows that such an analysis using Twitter data can provide a useful complement to existing household survey data and even potentially replace survey data if none were available. The finding show that when people tweet about the subsidy, they almost always do so in a negative manner; and there is a decline in discussion of topics about the reform subsidy, which coincides with increase in support for the subsidy as reported elsewhere. Therefore, the study concludes that decreasing discussion of the subsidy reform indicates an increase in support for the reform. In addition, the gas distributor strikes of May 2011 may have contributed to public perception of the reform more than previously acknowledged. This study is used as an opportunity to provide methodological guidance for researchers who wish to undertake similar studies, documenting the steps in the analysis pipeline with detail and noting the challenges inherent in obtaining data, classification, and inference.

The Philippines is one of the eight founding members of the open government partnership (OGP) alongside Brazil, Indonesia, Mexico, Norway, South Africa, United Kingdom, and the United States. The overarching concept of open government recognizes that good governance derives from the principle of transparency by providing an easily accessible, readily usable, and up-to-date online platform of digitized public records. Open data is an important component and commitment area of the OGP. The Philippines developed its first national open government action plan, which detailed nineteen initiatives under four broad outcome areas, from June to September 2011. This paper aims to: (1) document the historical development, key drivers, and milestones of open government Philippines and open data Philippines, and (2) pose recommendations for moving forward with its commitments. It reviews the composition and formation of the open data task force and showcases the features of data.gov.ph. The paper seeks to pose recommendations pertaining to the following areas: (1) release and manage organized, operable, and relevant data; (2) refine technical aspects of open data; (3) institutionalize open data within government; (4) promote civic engagement and stakeholder outreach; and (5) adopt complementary metrics and measures of success. The paper also opens a series of reports on the key stages in the development of the program, including implementation and impact evaluation.

This paper presents a formal ‘fragment’ of database semantics as a declarative model of a cognitive agent. It is called a Slim machine and functionally integrates the procedures of natural language interpretation, conceptualization, and production as well as query and inference. Each of these functions is illustrated explicitly by a corresponding LA-grammar. In addition, a control structure based on the principle of balance is presented. This principle mediates between the knowledge of the Slim machine and its current situation by selecting a suitable action.

The dynamic growth of mobile communications technology is creating opportunities for economic growth, social empowerment, and grassroots innovation in developing countries. One of the areas with the greatest potential impact is in the contribution that mobile applications can make to agricultural and rural development (ARD), by providing access to information, markets, and services to millions of rural inhabitants. For both agricultural supply and demand, mobile phones can reduce waste, make delivery more efficient, and forge closer links between farmers and consumers. This report provides policymakers and development practitioners with a guide that facilitates the development and deployment of mobile applications for ARD. It also informs their understanding of the key drivers for promoting such applications and services in their countries. Using James Moore’s (1996) revised definition of ecosystems: economic communities based on interacting organizations and individuals the report identifies a wide range of players in the ecosystem for m-ARD apps, such as mobile network operators, m-app (mobile applications) providers, content providers, and various types of users. M-apps are software designed to take advantage of mobile technology and can be developed for technology besides mobile phones. But mobile phones have many key advantages: affordability, wide ownership, voice communications, and instant and convenient service delivery. As a result, there has been a global explosion in the number of m-apps, facilitated by the rapid evolution of mobile networks and by the increasing functions and falling prices of mobile handsets. M-apps are markedly different in developing countries because they typically run on second-generation (2G) phones rather than smartphones, which are far more common in developed countries. The report reviews country examples and extracts policy lessons and good practices. It also presents detailed studies of cases from Kenya, Philippines, and Sri Lanka, as well as summarizes 92 case studies from Africa, Asia, and Latin America. The goal is to provide a comprehensive understanding of the development impact, ecosystem, and business models for mobile applications in ARD. The report is intended to complement the recent ICT in Agriculture eSourcebook. One of the main findings is that an enabling platform (or platforms) is probably the most important factor for the development of m-ARD apps. Platforms can facilitate interactions among ecosystem players, increase access to users, provide technical standards, and incorporate payment mechanisms.

International audience; We describe a generic framework for representing and reasoning with annotated Semantic Web data, a task becoming more important with the recent increased amount of inconsistent and non-reliable meta-data on the Web. We formalise the annotated language, the corresponding deductive system and address the query answering problem. Previous contributions on specific RDF annotation domains are encompassed by our unified reasoning formalism as we show by instantiating it on (i) temporal, (ii) fuzzy, and (iii) provenance annotations. Moreover, we provide a generic method for combining multiple annotation domains allowing to represent, e.g., temporally-annotated fuzzy RDF. Furthermore, we address the development of a query language – AnQL – that is inspired by SPARQL, including several features of SPARQL 1.1 (subqueries, aggregates, assignment, solution modifiers) along with the formal definitions of their semantics.

Geospatial semantic querying to geographical databases has been recognized as an hot topic in GIS research. Most approaches propose to adopt an ontology as a knowledge representation structure on top of the database, representing the concepts the user can query. These concepts are typically directly mapped to database tables. In this paper we propose a methodology where the ontology is further exploited mapping axioms to spatial SQL queries. The main advantage of this approach is that semantic-rich geospatial queries can be abstractly represented in the ontology and can be automatically translated into spatial SQL queries.

In this research will be worked through about how cryptography RC4's algorithm implementation in protection to query result and of query, security by encryption and descryption up to both is in network. Implementation of this research which is build software in client that function access databases that is placed by the side of server. Software that building to have facility for encryption and descryption query result and of query that is sent from client goes to server and. transmission query result and of query can secure its security. Well guaranted transmission security him of query result and of query can be told to succeed if success software can encryption query result and of query which transmission so that in the event of scanning to both, scanning will not understand data content. Conclusion of this research that is woke up software succeed encryption query and result of query which transmission between application of client and of server databases. Abstract in Bahasa Indonesia: Pada penelitian ini dibahas mengenai bagaimana mengimplementasikan algoritma kriptografi RC4 dalam proteksi terhadap query dan hasil query, pengamanan dilakukan dengan cara melakukan enkripsi dan dekripsi selama keduanya berada di dalam jaringan. Pengimplementasian dari penelitian ini yaitu membangun sebuah software yang akan diletakkan di sisi client yang berfungsi mengakses database yang diletakkan di sisi server. Software yang dibangun memiliki fasilitas untuk mengenkripsi dan mendektipsi query dan hasil query yang dikirimkan dari client ke server dan juga sebaliknya. Dengan demikian tramsmisi query dan hasil query dapat terjamin keamanannya.Terjaminnya keamanan transmisi query dan hasil query dapat dikatakan berhasil jika software berhasil mengenkripsi query dan hasil query yang ditransmisikan sehingga apabila terjadi penyadapan terhadap keduanya, penyadap tidak akan mengerti isi data tersebut. Kesimpulan dari penelitian ini yaitu software yang dibangun berhasil mengenkripsi query dan hasil query yang ditransmisikan antara aplikasi client dan server database. Kata kunci: enkripsi, transmisi, RC4, query, database

With a huge amount of RDF data available on the web, the ability to find and access relevant information is crucial. Traditional approaches to storing, querying, and reasoning fall short when faced with web-scale data. We present a system that combines the computational power of large clusters for enabling large-scale reasoning and data access with an efficient data structure for storing and querying the accessed data on a traditional personal computer or other resource-constrained device. We present results of using this system to load the 2009 Billion Triples Challenge dataset, materialize RDFS inferences, extract an ''interesting'' subset of the data using a large cluster, and further analyze the extracted data using a personal computer, all in the order of tens of minutes.

euzenat2009a; International audience; Relating ontologies is very important for many ontology-based applications and more important in open environments like the semantic web. The relations between ontology entities can be obtained by ontology matching and represented as alignments. Hence, alignments must be taken into account in ontology management. This chapter establishes the requirements for alignment management. After a brief introduction to matching and alignments, we justify the consideration of alignments as independent entities and provide the life cycle of alignments. We describe the important functions of editing, managing and exploiting alignments and illustrate them with existing components.

We consider the problem of discovering properties (such as the diameter) of an unknown network G(V,E) with a minimum number of queries. Initially, only the vertex set V of the network is known. Information about the edges and non-edges of the network can be obtained by querying nodes of the network. A query at a node q ∈ V returns the union of all shortest paths from q to all other nodes in V . We study the problem as an online problem – an algorithm does not initially know the edge set of the network, and has to decide where to make the next query based on the information that was gathered by previous queries. We study how many queries are needed to discover the diameter, a minimal dominating set, a maximal independent set, the minimum degree, and the maximum degree of the network. We also study the problem of deciding with a minimum number of queries whether the network is 2-edge or 2-vertex connected. We use the usual competitive analysis to evaluate the quality of online algorithms, i.e., we compare online algorithms with optimum offline algorithms. For all properties except maximal independent set, 2-vertex connectivity and minimum/maximum degree, we present and analyze online algorithms. Furthermore we show, for all the aforementioned properties, that “many” queries are needed in the worst case. As our query model delivers more information about the network than the measurement heuristics that are currently used in practise, these negative results suggest that a similar behavior can be expected in realistic settings, or in more realistic models derived from the all-shortest-paths query model., Technical Report, 591

We present an algorithm to solve XPath decision problems under regular tree type constraints and show its use to statically type-check XPath queries. To this end, we prove the decidability of a logic with converse for finite ordered trees whose time complexity is a simple exponential of the size of a formula. The logic corresponds to the alternation free modal μ-calculus without greatest fixpoint, restricted to finite trees, and where formulas are cycle-free. Our proof method is based on two auxiliary results. First, XML regular tree types and XPath expressions have a linear translation to cycle-free formulas. Second, the least and greatest fixpoints are equivalent for finite trees, hence the logic is closed under negation. Building on these results, we describe a practical, effective system for solving the satisfiability of a formula. The system has been experimented with some decision problems such as XPath emptiness, containment, overlap, and coverage, with or without type constraints. The benefit of the approach is that our system can be effectively used in static analyzers for programming languages manipulating both XPath expressions and XML type annotations (as input and output types).

Background: Internet search queries have become an important data source in syndromic surveillance system. However, there is currently no syndromic surveillance system using Internet search query data in South Korea. Objectives: The objective of this study was to examine correlations between our cumulative query method and national influenza surveillance data. Methods: Our study was based on the local search engine, Daum (approximately 25% market share), and influenza-like illness (ILI) data from the Korea Centers for Disease Control and Prevention. A quota sampling survey was conducted with 200 participants to obtain popular queries. We divided the study period into two sets: Set 1 (the 2009/10 epidemiological year for development set 1 and 2010/11 for validation set 1) and Set 2 (2010/11 for development Set 2 and 2011/12 for validation Set 2). Pearson’s correlation coefficients were calculated between the Daum data and the ILI data for the development set. We selected the combined queries for which the correlation coefficients were .7 or higher and listed them in descending order. Then, we created a cumulative query method n representing the number of cumulative combined queries in descending order of the correlation coefficient. Results: In validation set 1, 13 cumulative query methods were applied, and 8 had higher correlation coefficients (min=.916, max=.943) than that of the highest single combined query. Further, 11 of 13 cumulative query methods had an r value of ≥.7, but 4 of 13 combined queries had an r value of ≥.7. In validation set 2, 8 of 15 cumulative query methods showed higher correlation coefficients (min=.975, max=.987) than that of the highest single combined query. All 15 cumulative query methods had an r value of ≥.7, but 6 of 15 combined queries had an r value of ≥.7. Conclusions: Cumulative query method showed relatively higher correlation with national influenza surveillance data than combined queries in the development and validation set. [J Med Internet Res 2014;16(12):e289]

The effective processing of semi-structured data queries is a preliminary part of data mining stage. XML queries employ regular path expressions to find structural patterns within XML documents. The operation of structural join is a crucial part of XML query processing. Existing approaches reduce complex join expressions to several binary structural joins. In this paper, we are proposing a new structural join algorithm called sequence join algorithm, for sequential regular path expressions in securing XML query processing storage. It exploits information about position of the elements in a product to skip generation of the redundant intermediate lists. This paper further discusses the algorithm that performs the merge of several input lists of nodes in one pass. We carried out comparative experiments, and the results prove that the algorithm is better than multiple binary joins algorithm for queries of both small and large cardinality.

This study reviews recent experience applying ICTs in the fight against rural poverty and identifies ways in which donor interventions can make effective use of ICTs to improve the living conditions of rural communities in Latin America and the Caribbean and help reduce poverty.

Background: Drug Information Center (DIC) is an information center which provides drug information (DI) to healthcare professionals. The aim was to evaluate the performance of DIC for improving the quality and quantity of information services provided to the healthcare professionals. The service was provided free of cost to the customers. Materials and Methods: This descriptive study was conducted for the period of 6 months from February to August 2011 excluding May due to vacation. Customers were asked: how did they find the service provided to them? Was it good, satisfactory, or need improvement? There were written feedback forms to be filled by the customers, including customer satisfaction questions. The official publication of the DIC, RIPER PDIC Bulletin was screened for its types of articles/number of drug news published. The bulletin is circulated for free to the healthcare professionals electronically. Results and Discussion: A total of 232 queries were obtained during the study period of 6 months. Average number of queried received to the DIC was 39 per month. Most preferred mode of queries was personal access (89%). Majority of queries were received from nurses, i.e., 162 (70%) queries and 81% of all queries were drug oriented for improving knowledge. There were only 19% of the queries for individual patients; doctors asked most of those queries. Only 3% queries answered were rated as need improvement by the healthcare professionals. Rest were considered as either Good (56%) or satisfactory (49%). Range of drug news published in each bulletin was 3–4 and most of the other articles include expert opinion to improve practice or training. Conclusion: The DI services were satisfactorily used for academic interests. Nurses used the service for the highest compared to other health care professionals. Future studies should plan to establish the usefulness of DI to improve healthcare practice.

The paper introduces a model of the Web as an infinite, semistructured set of objects. We reconsider the classical notions of genericity and computability of queries in this new context and relate them to styles of computation prevalent on the Web, based on browsing and searching. We revisit several well-known declarative query languages (first-order logic, Datalog, and Datalog with negation) and consider their computational characteristics in terms of the notions introduced in this paper. In particular, we are interested in languages or fragments thereof which can be implemented by browsing, or by browsing and searching combined. Surprisingly, stratified and well-founded semantics for negation turn out to have basic shortcomings in this context, while inflationary semantics emerges as an appealing alternative.

Context-awareness is becoming an essential feature of services in smart environments in which various computing and sensing devices and position technologies are involved. Location, the most essential part of contextual information, is useful in many applications for determining position, navigation, routing, and tracking. There can be focus on particular application-specific services, e.g., user navigation for visualizing locations on maps and data providing information relevant to the user's current location. It is difficult for existing approaches to offer services as they are not initially designed as client's location is changing continuously and services need to be adapted accordingly. We have designed an architecture that supports automatic adaptation by triggering and discovery services for applications for timely need of user. We will try to give general service quality architecture with XML and XSD(schema) language for describing general services for applications.

Software Debugging is still one of the most challenging and time consuming aspects of software development. Monitoring the software behavior and finding the causes of this behavior are located at the center of debugging process. Although many tools and techniques have been proposed to support developers in this job, none of them could replace or improve the traditional debugging methods. This paper presents Query-Point debugging as a new debugging approach and explains how it can facilitate debugging for developers.