Query Templates Research Articles

A Split-Path Schema-Based RFID Data Storage Model in Supply Chain Management

In modern supply chain management systems, Radio Frequency IDentification (RFID) technology has become an indispensable sensor technology and massive RFID data sets are expected to become commonplace. More and more space and time are needed to store and process such huge amounts of RFID data, and there is an increasing realization that the existing approaches cannot satisfy the requirements of RFID data management. In this paper, we present a split-path schema-based RFID data storage model. With a data separation mechanism, the massive RFID data produced in supply chain management systems can be stored and processed more efficiently. Then a tree structure-based path splitting approach is proposed to intelligently and automatically split the movement paths of products. Furthermore, based on the proposed new storage model, we design the relational schema to store the path information and time information of tags, and some typical query templates and SQL statements are defined. Finally, we conduct various experiments to measure the effect and performance of our model and demonstrate that it performs significantly better than the baseline approach in both the data expression and path-oriented RFID data query performance.

Semantic Query-manipulation and Personalized Retrieval of Health, Food and Nutrition Information

Semantic manipulation of website content is important in many domains, but it is critical in some domains, such as health and nutrition. In such domains, users need to retrieve precise, trusted, and relevant health and food information. Even with a high-quality, semantic, Web-based search engine, it is not enough for retrieving the precise health- and nutrition-related information. That is because the retrieved information might not fit the user's specific needs due to the huge amount of information scattered throughout the Web. Thus, semantic query manipulation and personalization techniques will help and guide users in retrieving more relevant health and nutrition information consistent with their needs. In this paper, we present our efforts to develop a framework for semantic query manipulation and personalization of health and nutrition information. We propose a user profile ontology based on culture, language, health and nutrition. The profile is used to enrich the query and to personalize the retrieved health and food information to be consistent with the user's needs. Moreover, we propose query templates that are used for semantic manipulation and mapping of the user's natural language queries into ontology-based queries. We have implemented the proposed framework, and the empirical evaluations show promising improvements in the relevancy of the retrieved results and of the user's satisfaction.

Genomic and proteomic data integration for comprehensive biodata search

Genomic and proteomic data integration for comprehensive biodata search

DETECTION OF FRAME DUPLICATION FORGERY IN VIDEOS BASED ON SPATIAL AND TEMPORAL ANALYSIS

In this paper, we present a passive-blind scheme for detection of frame duplication forgery in videos. The scheme is a coarse-to-fine approach that is implemented in four stages: candidate segment selection, spatial similarity measurement, frame duplication classification, and post-processing. To screen and select duplicated candidates in the temporal domain, the histogram difference of two adjacent frames in the RGB color space is adopted as a feature. Then, to evaluate the similarity of two images, we use a block-based algorithm to measure the spatial correlation between the candidate segment and the corresponding frame in the query template. Based on the results of spatial and temporal analysis, we construct a classifier to detect duplicated clips. In addition, to deal with the partial detection problem, we develop a post-processing technique that examines and merges two adjacent detected candidates into a complete duplicated video clip. Our experiment results demonstrate that the proposed scheme can not only achieve detection of frame duplication forgery but also accurately detect and localize duplicated clips in different kinds of videos. The results also show that the scheme outperforms an existing method in terms of precision, recall, accuracy, and computation time.

Iris Data Indexing Method Using Gabor Energy Features

Biometric features are extracted from a complex pattern and stored as high dimensional data. These data do not follow traditional sorting order like numerical and alphabetical data. Hence, a linear search method makes the identification process extremely slow as well as increases the false acceptance rate beyond an acceptable range. To address this problem, we propose an efficient indexing mechanism to retrieve iris biometric templates using Gabor energy features. The Gabor energy features are calculated from the preprocessed iris texture in different scales and orientations to generate a 12-dimensional index key for an iris template. An index space is created based on the values of index keys of all individuals. A candidate set is retrieved from the index space based on the values of query index key. Next, we rank the retrieved candidates according to their occurrences. If the identity of the query template is matched, then it is a hit, otherwise a miss. We have experimented our approach with Bath, CASIA-V3-Interval, CASIA-V4-Thousand, MMU2, and WVU iris databases. Our proposed approach gives 11.3%, 14.5%, 16.3%, 13.5%, and 10.3% penetration rates and 98.2%, 91.1%, 90.7%, 85.2%, and 96% hit rates for Bath, CASIA-V3-Interval, CASIA-V4-Thousand, MMU2, and WVU iris database, respectively, when we consider the retrieving templates up to the fifth rank. Experiments substantiate that our approach is capable of retrieving biometric data with a higher hit rate and lower penetration rate compared to the existing approaches. Application of Gabor energy features to index iris data proves to be effective for fast and accurate retrieval. With our proposed approach, it is possible to retrieve a set of iris templates similar to the query template in the order of milliseconds and is independent of sizes of databases.

A Novel Automated Shoeprint Matching Technique for use as Forensic Evidence in Criminal Investigation

This work presents a technique in which a database of outsole pattern of shoeprint images has been automatically sorted against a query shoeprint image. Shoe marks found at the place of crime are used to provide valuable forensic evidence. This system presents a technique for rotation and intensity invariant automatic shoeprint matching so that the spatial positioning of the reference shoeprint image does not have to correspond with the spatial positioning of the shoeprint images of the database. Gabor transform has been used to extract multi resolution features of a shoeprint. Radon transform has been used to estimate the rotation of the shoeprint image and is compensated by rotating the features in opposite direction. Shoeprint database has been generated by inviting participants to tread on an inkpad and then stamp on a piece of paper. Template query images have been compared using Euclidean distance classifier which has been used to find a suitable match. The performance of the proposed algorithm has been evaluated in terms of percentage accuracy for four different matching methods. This technique performs better compared to results obtained using power spectral density features for full print images with the rotation, intensity and mixed attacks. General Terms Image processing, Forensic application.

Exploring Aromatic Chemical Space with NEAT: Novel and Electronically Equivalent Aromatic Template

In this paper, we describe a lead transformation tool, NEAT (Novel and Electronically equivalent Aromatic Template), which can help identify novel aromatic rings that are estimated to have similar electrostatic potentials, dipoles, and hydrogen bonding capabilities to a query template; hence, they may offer similar bioactivity profiles. In this work, we built a comprehensive heteroaryl database, and precalculated high-level quantum mechanical (QM) properties, including electrostatic potential charges, hydrogen bonding ability, dipole moments, chemical reactivity, and othe properties. NEAT bioisosteric similarities are based on the electrostatic potential surface calculated by Brood, using the precalculated QM ESP charges and other QM properties. Compared with existing commercial lead transformation software, (1) NEAT is the only one that covers the comprehensive heteroaryl chemical space, and (2) NEAT offers a better characterization of novel aryl cores by using high-evel QM properties that are relevant to molecular interactions. NEAT provides unique value to medicinal chemists quickly exploring the largely uncharted aromatic chemical space, and one successful example of its application is discussed herein.

RFID Data Processing in Supply Chain Management Using a Path Encoding Scheme

RFID technology can be applied to a broad range of areas. In particular, RFID is very useful in the area of business, such as supply chain management. However, the amount of RFID data in such an environment is huge. Therefore, much time is needed to extract valuable information from RFID data for supply chain management. In this paper, we present an efficient method to process a massive amount of RFID data for supply chain management. We first define query templates to analyze the supply chain. We then propose an effective path encoding scheme that encodes the flows of products. However, if the flows are long, the numbers in the path encoding scheme that correspond to the flows will be very large. We solve this by providing a method that divides flows. To retrieve the time information for products efficiently, we utilize a numbering scheme for the XML area. Based on the path encoding scheme and the numbering scheme, we devise a storage scheme that can process tracking queries and path oriented queries efficiently on an RDBMS. Finally, we propose a method that translates the queries to SQL queries. Experimental results show that our approach can process the queries efficiently.

SHAFTS: A Hybrid Approach for 3D Molecular Similarity Calculation. 2. Prospective Case Study in the Discovery of Diverse p90 Ribosomal S6 Protein Kinase 2 Inhibitors To Suppress Cell Migration

We described a prospective application of ligand-based virtual screening program SHAFTS to discover novel inhibitors for p90 ribosomal S6 protein kinase 2 (RSK2). Taking the putative 3D conformations of two weakly binding RSK2 NTKD inhibitors as query templates, SHAFTS was used to perform 3D similarity based virtual screening because of a lack of crystal structure of RSK2 protein, thus leading to the identification of several novel scaffolds that would have been missed by conventional 2D fingerprint methods. The most potent hit compounds show low micromolar inhibitory activities against RSK2. In particular, one of the hit compounds exhibits potent antimigration activity against the MDA-MB-231 tumor cell. The results exemplified SHAFTS' application in active enrichment and scaffold hopping, which is of general interest for lead identification in drug discovery endeavors and also provides novel scaffolds that lay the foundation for uncovering new RSK2 regulatory mechanisms.

A Query-based Approach for Semi-Automatic Annotation of Web Services

Semantic Web services (SWS) have attracted increasing attention due to their potential to automate discovery and composition of current syntactic Web services. An issue that prevents a wider adoption of SWS relates to the manual nature of the semantic annotation task. Manual annotation is a difficult, error-prone, and time-consuming process and automating the process is highly desirable. Though some approaches have been proposed to semi-automate the annotation task, they are difficult to use and cannot perform accurate annotation for the following reasons: (1) They require building application ontologies to represent candidate services and (2) they cannot perform accurate name-based matching when labels of candidate service elements and ontological entities contain Compound Nouns (CN). To overcome these two deficiencies, this paper proposes a query-based approach that can facilitate semi-automatic annotation of Web services. The proposed approach is easy to use because it does not require building application ontologies to represent services. Candidate service elements that need to be annotated are extracted from a WSDL file and used to generate query instances by filling a Standard Query Template. The resulting query instances are executed against a repository of ontologies using a novel query execution engine to find appropriate correspondences for candidate service elements. This query execution engine employs name-based and structural matching mechanisms that can perform effective and accurate similarity measurements between labels containing CNs. The proposed semi-automatic annotation approach is evaluated by employing it to annotate existing Web services using published domain ontologies. Precision and recall are used as evaluation metrics. The resulting precision and recall values demonstrate the effectiveness and applicability of the proposed approach.

Constraint based frequent pattern mining for generalized query templates from web log

The World-Wide Web provides every Internet citizen access to an abundance of information, but difficulty increases in identifying the relevant piece of information. Popular Search engine uses log for keeping track of user activities including user queries, click-through and their behavior. Research in web mining tries to address this problem by discovering knowledge from user logs. We propose an approach to discover patterns that can predict user’s search, without aid of remote server. Our method analyses user’s interactions by constructing FP-tree, which facilitates in producing templates from the user logs. Consensus tree growth is restricted and templates are obtained from leaves, which assist user’s searching process with precision. We show the effectiveness of our method on realistic web logs and explore the tradeoff between prediction’s accuracy and usefulness. Test results show the improved algorithm has lower complexity of time and space, and fit the capacity of memory.

An Ontology-based Question Answering Method with the use of Query Template

paper presents a new method for ontology-based question answering (QA) with the use of Query template for Dining Ontology as the domain (Service of the restaurant is called dining). Hypothesis questions and query templates can be produced from domain ontology. Pre-generation of hypothesis questions on the basis of the domain ontology and textual entailment. Semantic web is the expression of meaning of the content. Semantic web based gives instant access. Reusability is enhanced. This method is not domain specific. It focuses on the RDF, Ontology model and Web ontology language. Design of the ontology created can be viewed. Query processing is faster using text pool, dining ontology is developed. Using RDF ontology, every provider is free to add or subclass concepts from the initial version without being at risk to become incompatible. The ontology document is present in RDF languages. Query templates used for data extraction. RDF is an abstract model with several serialization format, so particularly resource are triple is encoded varies from format to format in (2). The RDF data model is based on a class diagram, and it make statements, about resources in the form of subject-predicate-object expressions. This expressions are known as triples. The subject denotes the resource, the predicate denotes aspects of the resource and expressed a relationship between the subject and the object. This project deals with creation of ontology for restaurant operation, that is service of the restaurant may be called as dining, hence dining ontology created.

On the stability of plan costs and the costs of plan stability

Predicate selectivity estimates are subject to considerable run-time variation relative to their compile-time estimates, often leading to poor plan choices that cause inflated response times. We present here a parametrized family of plan generation and selection algorithms that replace, whenever feasible, the optimizer's solely cost-conscious choice with an alternative plan that is (a) guaranteed to be near-optimal in the absence of selectivity estimation errors, and (b) likely to deliver comparatively stable performance in the presence of arbitrary errors. These algorithms have been implemented within the PostgreSQL optimizer, and their performance evaluated on a rich spectrum of TPC-H and TPC-DS-based query templates in a variety of database environments. Our experimental results indicate that it is indeed possible to identify robust plan choices that substantially curtail the adverse effects of erroneous selectivity estimates. In fact, the plan selection quality provided by our algorithms is often competitive with those obtained through apriori knowledge of the plan search and optimality spaces. The additional computational overheads incurred by the replacement approach are miniscule in comparison to the expected savings in query execution times. We also demonstrate that with appropriate parameter choices, it is feasible to directly produce anorexic plan diagrams, a potent objective in query optimizer design.

Modeling and querying provenance by extending CIDOC CRM

This paper elaborates on the problem of modeling provenance for both physical and digital objects. In particular it discusses provenance according to OAIS (ISO 14721:2003) and how it relates with the conceptualization of CIDOC CRM ontology (ISO 21127:2006). Subsequently it introduces an extension of the CIDOC CRM ontology, able to capture the modeling and the query requirements regarding the provenance of digital objects. Over this extension the paper provides a number of indicative examples of modeling provenance in various domains. Subsequently, it introduces a number of indicative provenance query templates, and finally it describes an implementation using Semantic Web technologies.

Querying Ontology using Keywords and Quantitative Restriction Phrases

Many approaches for converting keyword queries to formal query languages are presented for natural language interfaces to ontologies. Some approaches present fixed formal query templates, so they lack in providing support with increasing number of words in the user query. Other approaches work on constructing and manipulating subgraphs from RDF graphs so their processing is complex with respect to time and space. Techniques are presented to perform operations by obtaining a reduced RDF graph but they limit the input to some type of resources so their complete complexity with all type of input resources is unknown. For formal query generation, we present a variable query template whose computation is facilitated by less complex and distributed RDF property and relation graphs. A prototype QuriOnto is developed to evaluate our design. The user can query QuriOnto with any number of words and resource types. Also, to the best of our knowledge, it is the first system that can handle quantitative restrictions with keyword queries. As QuriOnto has no support for semantic similarity at this time except for rdfs labels so its recall is low but high precision shows that the approach is promising for the generation of corresponding formal queries.

A proposed architecture for dynamically built NLIDB systems

Natural Language Interface to Database (NLIDB) systems have provided an easy access to database system without the need for the user to use formal query languages, such as SQL. Database query languages can be difficult to the non-expert users and learning these formal queries takes a lot of time. In NLIDB, users can type a question or a sentence in their natural language (such as English or Arabic) then it will be converted through a special natural language interface interrupter into formal database query. A major problem that faces the NLIDB designers is the identification of the tables that contain the required information and the desired attributes in the query. Most of the existing systems use static templates for the queries, in which tables' names are embedded in the template. However, this requires large code that considers all possible query templates. The main objective of this paper is to introduce a dynamic approach to determine the tables name that the attributes involved in the query belong to by representing the database schema as a graph which is used to determine the tables' names. This will minimize the templates used to build the queries thus minimizing the code size and effort to track and build all possible queries. We also propose a dynamic component that helps in building any NLIDB called database management. This component is linked to all the NLIDB system's components and will automatically build the dictionaries and database schema graph. The proposed approach was applied to a case study called Mall Shopper's Guide (MSG) to investigate its effectiveness.

Articulating complex information needs using query templates

In this paper we investigate the effectiveness of topic-independent query templates as a tool for assisting users in articulating their information needs. We hypothesize that topic-independent query templates can help users with complex information needs to express their requirements more accurately and in greater detail. We developed a set of query templates representing general semantic relationships between concepts, such as cause—effect and problem—solution. Each template was written in the form of a fill-in-the-blanks question. A user study was performed comparing the template-based interface with a single-textbox search interface. Results demonstrate that, while users found the template-based query formulation less easy to use, the queries written using templates performed better than the queries written using the control interface with one query textbox.

Empirical Capacity of a Recognition Channel for Single- and Multipose Object Recognition Under the Constraint of PCA Encoding

The ability of practical recognition systems to recognize a large number of objects is constrained by a variety of factors that include choice of a feature extraction technique, quality of images, complexity and variability of underlying objects and of collected data. Given a feature extraction technique generating templates of objects from data and a resolution of the original images, the remaining factors can be attributed to distortions due to a recognition channel. We define the recognition channel as the environment that transforms reference templates of objects in a database into templates submitted for recognition. If templates in an object database are generated to be statistically independent and the noise in a query template is statistically independent of templates in the database, then the abilities of the recognition channel to recognize a large number of object classes can be characterized by a number called recognition capacity. In this paper, we evaluate the empirical recognition capacity of PCA-based object recognition systems. The encoded data (templates) and the additive noise in query templates are modeled to be Gaussian distributed with zero mean and estimated variances. We analyze both the case of a single encoded image and the case of encoded correlated multiple images. For this case, we propose a model that is orientation and elevation angle (pose) dependent. The fit of proposed models is judged using statistical goodness of fit tests. We define recognition rate as the ratio R=log(M)/n, where M is the number of objects to recognize and n is the length of PCA templates. The empirical capacity of PCA-based recognition systems is numerically evaluated. The empirical random coding exponent is also numerically evaluated and plotted as a function of the recognition rate. With these results, given a value of the recognition capacity and the length of templates (assume large), we can predict the number of distinct object classes that can be stored in an object library and be identified with probability of error close to zero.

Scaling the walls of discovery: using semantic metadata for integrative problem solving

Current data integration approaches by bioinformaticians frequently involve extracting data from a wide variety of public and private data repositories, each with a unique vocabulary and schema, via scripts. These separate data sets must then be normalized through the tedious and lengthy process of resolving naming differences and collecting information into a single view. Attempts to consolidate such diverse data using data warehouses or federated queries add significant complexity and have shown limitations in flexibility. The alternative of complete semantic integration of data requires a massive, sustained effort in mapping data types and maintaining ontologies. We focused instead on creating a data architecture that leverages semantic mapping of experimental metadata, to support the rapid prototyping of scientific discovery applications with the twin goals of reducing architectural complexity while still leveraging semantic technologies to provide flexibility, efficiency and more fully characterized data relationships. A metadata ontology was developed to describe our discovery process. A metadata repository was then created by mapping metadata from existing data sources into this ontology, generating RDF triples to describe the entities. Finally an interface to the repository was designed which provided not only search and browse capabilities but complex query templates that aggregate data from both RDF and RDBMS sources. We describe how this approach (i) allows scientists to discover and link relevant data across diverse data sources and (ii) provides a platform for development of integrative informatics applications.

A risk management approach to RBAC

Even if Role Based Access Control (RBAC) is employed properly, distributed database environments are still prone to illegitimate access attempts: in RBAC, users potentially carry the risk of illegal access attempts via credentials violation, or unintentional/intentional incorrect use of already granted permissions via role misuse/abuse. We introduce a probabilistic risk management model for enhanced access control in such databases. We incorporate failure modes and effects analysis (FMEA) scheme for measuring user risks in our design. We combine components as credentials, queries, role history logs and expected utility for a probabilistic formulation of risk. We present experimental results that we obtained on real world database. The results emphasize the need for a database where roles are well defined and queries under different roles do not overlap. We suggest using query templates with minimized role definitions so that the risk model we introduce provides better risk management.