Complex Schema Research Articles

Schema-based predictive eye movements support sequential memory encoding.

When forming a memory of an experience that is unfolding over time, we can use our schematic knowledge about the world (constructed based on many prior episodes) to predict what will transpire. We developed a novel paradigm to study how the development of a complex schema influences predictive processes during perception and impacts sequential memory. Participants learned to play a novel board game ('four-in-a-row') across six training sessions and repeatedly performed a memory test in which they watched and recalled sequences of moves from the game. We found that participants gradually became better at remembering sequences from the game as their schema developed, driven by improved accuracy for schema-consistent moves. Eye tracking revealed that increased predictive eye movements during encoding, which were most prevalent in expert players, were associated with better memory. Our results identify prediction as a mechanism by which schematic knowledge can improve episodic memory.

P3 Process for Object-Relational Data Migration to NoSQL Document-Oriented Datastore

The exponential growth of complex data in object-relational databases (ORDB) raises the need for efficient storage with scalability, consistency, and partition tolerance. The migration towards NoSQL (not only structured query language) datastores is the best fit for distributed complex data. Unfortunately, very few studies provide solutions for ORDB migration to NoSQL. This paper reports on how to achieve the migration of complex data from ORDB to a document-oriented NoSQL database. The proposed approach focused on the P3 process that involves three major stages: (P1) the preprocessing stage to access and extract the database features using SQL queries, (P2) the processing stage to provide the data mapping by using a list of mapping rules between the source and target models, and (P3) the post-processing stage to store and request the migrated data within the NoSQL context. A thorough experiments on two real-life databases veriðes the P3 process improves the performance of data migration with complex schema structures.

A metrics-based meta-learning model with meta-pretraining for industrial knowledge graph construction

Isolated data silos and domain-specific knowledge pose challenges for knowledge graph construction in the manufacturing industry, where heterogeneous storage leads to distributed databases with complex schemas. In this article, a resource-based industrial knowledge graph is developed using a few-shot classification algorithm to save on labor and other related costs in industrial knowledge graph construction, and an attribute-based fusion strategy for data fusion and alignment is designed. We also propose a novel metrics-based meta-learning model with meta-pretraining (MMM) to address the few-shot text classification problem. Experiment results show that MMM achieves 87.13% accuracy on the 5-shot text classification benchmark Amazon Review Sentiment Classification (ARSC), outperforming other baselines, such as Induction Networks (85.63%) and Distributional Signatures (81.16%). The MMM achieves a 34.6% accuracy improvement compared with Distributional Signatures (84.34% vs. 62.66%) on 1-shot problems of ARSC, hence highlighting the applicability of our model in low-resource conditions. Based on the proposed methods, we further develop an industrial knowledge graph platform with industrial applications, such as value chain analysis and collaboration, to improve knowledge reuse and service innovation.

Fine-grained spatial information extraction in radiology as two-turn question answering

ObjectivesRadiology reports contain important clinical information that can be used to automatically construct fine-grained labels for applications requiring deep phenotyping. We propose a two-turn question answering (QA) method based on a transformer language model, BERT, for extracting detailed spatial information from radiology reports. We aim to demonstrate the advantage that a multi-turn QA framework provides over sequence-based methods for extracting fine-grained information. MethodsOur proposed method identifies spatial and descriptor information by answering queries given a radiology report text. We frame the extraction problem such that all the main radiology entities (e.g., finding, device, anatomy) and the spatial trigger terms (denoting the presence of a spatial relation between finding/device and anatomical location) are identified in the first turn. In the subsequent turn, various other contextual information that acts as important spatial roles with respect to a spatial trigger term are extracted along with identifying the spatial and other descriptor terms qualifying a radiological entity. The queries are constructed using separate templates for the two turns and we employ two query variations in the second turn. ResultsWhen compared to the best-reported work on this task using a traditional sequence tagging method, the two-turn QA model exceeds its performance on every component. This includes promising improvements of 12, 13, and 12 points in the average F1 scores for identifying the spatial triggers, Figure, and Ground frame elements, respectively. DiscussionOur experiments suggest that incorporating domain knowledge in the query (a general description about a frame element) helps in obtaining better results for some of the spatial and descriptive frame elements, especially in the case of the clinical pre-trained BERT model. We further highlight that the two-turn QA approach fits well for extracting information for complex schema where the objective is to identify all the frame elements linked to each spatial trigger and finding/device/anatomy entity, thereby enabling the extraction of more comprehensive information in the radiology domain. ConclusionExtracting fine-grained spatial information from text in the form of answering natural language queries holds potential in achieving better results when compared to more standard sequence labeling-based approaches.

Efficient processing of complex XSD using Hive and Spark.

The eXtensible Markup Language (XML) files are widely used by the industry due to their flexibility in representing numerous kinds of data. Multiple applications such as financial records, social networks, and mobile networks use complex XML schemas with nested types, contents, and/or extension bases on existing complex elements or large real-world files. A great number of these files are generated each day and this has influenced the development of Big Data tools for their parsing and reporting, such as Apache Hive and Apache Spark. For these reasons, multiple studies have proposed new techniques and evaluated the processing of XML files with Big Data systems. However, a more usual approach in such works involves the simplest XML schemas, even though, real data sets are composed of complex schemas. Therefore, to shed light on complex XML schema processing for real-life applications with Big Data tools, we present an approach that combines three techniques. This comprises three main methods for parsing XML files: cataloging, deserialization, and positional explode. For cataloging, the elements of the XML schema are mapped into root, arrays, structures, values, and attributes. Based on these elements, the deserialization and positional explode are straightforwardly implemented. To demonstrate the validity of our proposal, we develop a case study by implementing a test environment to illustrate the methods using real data sets provided from performance management of two mobile network vendors. Our main results state the validity of the proposed method for different versions of Apache Hive and Apache Spark, obtain the query execution times for Apache Hive internal and external tables and Apache Spark data frames, and compare the query performance in Apache Hive with that of Apache Spark. Another contribution made is a case study in which a novel solution is proposed for data analysis in the performance management systems of mobile networks.

A novel measure of matching categories for early development: Item creation and pilot feasibility study

BackgroundMany cognitive tests assess a limited developmental span, making longitudinal measurement for trials aimed at improving cognition challenging. Tests targeting transitional skills, which integrate foundational abilities into complex schemas, may be amenable to assessment across a wide developmental span. Furthermore, tablet-based tests permit computer adaptive testing (CAT), which is psychometrically more efficient and could increase testing motivation, especially for children with developmental delays. Such measures may be useful for research and clinical practice. AimsOutline the creation of a novel, tablet-based concept formation test, and evaluate its feasibility in individuals with mental ages less than 24-months. Methods and proceduresItem generation, user interface construction, and pre-piloting were conducted in consultation with subject matter experts. Item content and interface parameters underwent iterative revisions, resulting in the pilot test. Outcomes and resultsWe created and piloted a tablet-based test of concept formation suitable for CAT-based administration with items of increasing difficulty based on target salience. We show feasibility in individuals with mental ages less than 24-months-old. Conclusions and implicationsTablet-based assessment of concept formation may be a useful outcome measure of an aspect of cognitive ability in young children. Future work will address optimizing the user interface and developing CAT administration.

The Forgotten Document-Oriented Database Management Systems: An Overview and Benchmark of Native XML DODBMSes in Comparison with JSON DODBMSes

In the current context of Big Data, a multitude of new NoSQL solutions for storing, managing, and extracting information and patterns from semi-structured data have been proposed and implemented. These solutions were developed to relieve the issue of rigid data structures present in relational databases, by introducing semi-structured and flexible schema design. As current data generated by different sources and devices, especially from IoT sensors and actuators, use either XML or JSON format, depending on the application, database technologies that store and query semi-structured data in XML format are needed. Thus, Native XML Databases, which were initially designed to manipulate XML data using standardized querying languages, i.e., XQuery and XPath, were rebranded as NoSQL Document-Oriented Databases Systems. Currently, the majority of these solutions have been replaced with the more modern JSON based Database Management Systems. However, we believe that XML-based solutions can still deliver performance in executing complex queries on heterogeneous collections. Unfortunately nowadays, research lacks a clear comparison of the scalability and performance for database technologies that store and query documents in XML versus the more modern JSON format. Moreover, to the best of our knowledge, there are no Big Data-compliant benchmarks for such database technologies. In this paper, we present a comparison for selected Document-Oriented Database Systems that either use the XML format to encode documents, i.e., BaseX, eXist-db, and Sedna, or the JSON format, i.e., MongoDB, CouchDB, and Couchbase. To underline the performance differences we also propose a benchmark that uses a heterogeneous complex schema on a large DBLP corpus.

Examples: A Pragmatic Approach

This study deals with a seemingly obvious topic to everyone – examples. Yet, on a closer perusal, the topic turns out to be interestingly perplexed. This can be justified by the amalgam of cognition and communication out of which such concept is delivered. This mixture gives a hand in texturizing examples. This work aims at pragmatically investigating the concept of ‘example’ as far as its definition, structure, types and functions are concerned. Furthermore, it aims at developing an eclectic model that will be utilized to pragmatically analyze the data of the work represented by four interviews with Donald Trump in 2018. Consequently, it hypothesizes the following: complex schema is more frequently used in the data; abductive inference is never employed in the data; faulty analogy is never made in the data; the interpersonal variant of the global type of examples is more frequently found in the data; explicit examples are more commonly given; and, finally, the argumentative function of examples prevails. Afterwards, the percentage equation is employed to statistically calculate the results of the analysis. 
 The study has come up with many conclusions, out of which is that examples, once chosen meticulously, support the cogency of argumentation by means of strengthening and boosting one’s standpoints towards the alleged goals.

Exploring the Relationship between Ability Grouping and Science Vocabulary Learning

This study investigated 8th-grade science students (13-14 year-olds) in advanced and traditional ability classrooms perceptions of their vocabulary knowledge, learning, and content achievement. This research acknowledges the critical role that developing vocabulary knowledge from reading plays in gaining a deeper content understanding as well as promoting overall science literacy. Data sources included pre and posttest of students’ perceptions of vocabulary knowledge, students’ perceptions of vocabulary and reading strategies surveys, and a content achievement test. Students’ perceptions of vocabulary knowledge were compared before and after instruction to see whether students believed they gained knowledge and the ability to explain categories of technical science terms. Students’ in both advanced and traditional classrooms perceptions of vocabulary knowledge increased as a result of instruction. The participants had favorable views of the vocabulary and reading strategies implemented and believed the literacy approaches were important for their developing scientific knowledge. Also, students’ content achievement was compared for the advanced and traditional groups. Although advanced class classes outperformed traditional courses on the content test, only one of the seven items was statistically significant. Both groups were compared to a national data set that served as a control and had statistically significant higher achievement than the national data set. Most of the inferential measures indicate that there is no statistically significant difference between the two groups. The combination of metacognitive awareness, close reading, and integrative vocabulary helped all students build complex schema surrounding science vocabulary as evidenced by student’s perceptions of knowledge and their content achievement. This study is one of the first to highlight the pivotal role students’ perception of vocabulary knowledge and vocabulary and reading strategies plays in science content learning regardless of ability level.

Example-driven query intent discovery

Traditional relational data interfaces require precise structured queries over potentially complex schemas. These rigid data retrieval mechanisms pose hurdles for non-expert users, who typically lack language expertise and are unfamiliar with the details of the schema. Query by Example (QBE) methods offer an alternative mechanism: users provide examples of their intended query output and the QBE system needs to infer the intended query. However, these approaches focus on the structural similarity of the examples and ignore the richer context present in the data. As a result, they typically produce queries that are too general, and fail to capture the user's intent effectively. In this paper, we present SQuID, a system that performs semantic similarity-aware query intent discovery . Our work makes the following contributions: (1) We design an end-to-end system that automatically formulates select-project-join queries in an open-world setting, with optional group-by aggregation and intersection operators; a much larger class than prior QBE techniques. (2) We express the problem of query intent discovery using a probabilistic abduction model , that infers a query as the most likely explanation of the provided examples. (3) We introduce the notion of an abduction-ready database, which precomputes semantic properties and related statistics, allowing SQuID to achieve real-time performance. (4) We present an extensive empirical evaluation on three real-world datasets, including user-intent case studies, demonstrating that SQuID is efficient and effective, and outperforms machine learning methods, as well as the state-of-the-art in the related query reverse engineering problem.

Topologies of schemes of active dynamic compounds of objects

The research results of methods for constructing schematics of objects compounds used to create software with architecture based on integral objects are presents in the paper. The basis for determining the main topologies of the compounds schemes is the major elements of compounds classification, including the types of objects of compounds, types of connections and types of calculations that the compounds implements. The objects from which are built compounds were divided into four types: data, functions, switches, and activators. Data objects are represented by variable and constant objects that store data. Function objects perform transformations of input data into output results, implementing a specific function or composition of functions. Switch objects perform the function of dynamic connections control within fixed connections in the scheme and are represented by classes of interrupters, switches, and selective switches. Activator objects are intended for constructing schemes that require multiple or sequential activations of objects and are represented by classes of cyclic and step-by-step activators. Types of connections between objects in compounds are divided into three types: active or passive, fixed or switched, forward or backward. Activity or passivity of connections is determined by the output connector’s type, involved in a particular connection. The fixity or commutation of connections is determined by the absence or presence of switches in the compound scheme. Feedback allows transferring object data from its output to its input or to the input of previous objects or to use recursion. The calculations types that implement by compounds are divided into three types: functional, iterative, and step-by-step calculations. Functional calculations take place when the states of objects are not used. Iterative calculations require the reactivation of the same objects in the process of calculating the result, and step-by-step schemes sequentially activate various objects of the scheme in a defined order. Based on the developed classification of the compounds scheme’s elements, three types of basic topologies were defined: combinational schemes, feedback schemes and schemes with a controller. Combination schemes implement functional calculations. Feedback schemes can implement both functional and iterative (recursive) calculations. Schemes with a controller used for activators to implement iterative and step-by-step calculations. By combining basic topologies, complex schemas can be obtained

Structural accommodations of patriarchy: Women and workplace gender segregation in Qatar

As the institutions of classic patriarchy erode in Qatar, women are entering the labour force in growing numbers. It is argued that women's need to work in societies historically characterized by classic patriarchy causes them to enact strategic accommodations that signal their feminine respectability and conformity to male domination. We find the Qatari context to be characterized by structural rather than individual accommodations of patriarchy. State institutions and several employers have made available gender‐segregated workplaces that facilitate women's employment while maintaining many elements of patriarchy. Using semi‐structured interview data with university‐aged Qatari women, we examine attitudes towards employment, specifically those related to gender mixing in the workplace. Young women's narratives reveal complex schemas regarding the acceptability of gender mixing, which depends on characteristics of the working woman, characteristics of the men with whom she must interact in the workplace and the spatial organization of the workplace itself. Women's protection of their reputations, critical to maintaining their families’ support and their own marriageability, emerged as a key motivation for limiting interactions with men. The preference for gender‐segregated workplaces reveals Qatari women's continued subscription to the patriarchal bargain — they constrain their behaviour in return for protection from male kin.

Domain and Schema Independent Semantic Model Verbalization: A Conceptual Overview

Semantic Web-based technologies have become extremely popular and its a success that has spread across many domains, additional to the computer science domain. Nevertheless, the reusability aspects associated with the created and available semantic knowledge models are very low. The main bottleneck associated with this issue is, the difficulty associated in understanding the complex schema of a knowledge model created and barriers associated with querying the knowledge models using SPARQL or SQWRL query formulations. This research emphasizes on proposing a verbalizer which can go beyond existing Controlled Natural Language (CNL) type verbalizers and to verbalizer knowledge stored in a knowledge model file written in either RDF or OWL format, despite its domain and schematics.

CERES

The web contains countless semi-structured websites, which can be a rich source of information for populating knowledge bases. Existing methods for extracting relations from the DOM trees of semi-structured webpages can achieve high precision and recall only when manual annotations for each website are available. Although there have been efforts to learn extractors from automatically generated labels, these methods are not sufficiently robust to succeed in settings with complex schemas and information-rich websites. In this paper we present a new method for automatic extraction from semi-structured websites based on distant supervision. We automatically generate training labels by aligning an existing knowledge base with a website and leveraging the unique structural characteristics of semi-structured websites. We then train a classifier based on the potentially noisy and incomplete labels to predict new relation instances. Our method can compete with annotation-based techniques in the literature in terms of extraction quality. A large-scale experiment on over 400,000 pages from dozens of multi-lingual long-tail websites harvested 1.25 million facts at a precision of 90%.

Searching for Simplicity in the Complexity of Symptom Clusters.

Occam's razor proposes that the simplest solution is preferable; however, in the science of cancer and cancer symptoms, simple solutions have been elusive. Understanding individual symptoms continues to confound the science community, and symptom cluster science appears to be an incredibly complex schema of individual symptoms co-occurring and interacting with one another. The simplest explanation for symptom clusters would be a common underlying mechanism at the human genome level. Such a finding could greatly simplify and perhaps revolutionize symptom management.

Mental logic and its difficulties with disjunction

The mental logic theory does not accept the disjunction introduction rule of standard propositional calculus as a natural schema of the human mind. In this way, the problem that I want to show in this paper is that, however, that theory does admit another much more complex schema in which the mentioned rule must be used as a previous step. So, I try to argue that this is a very important problem that the mental logic theory needs to solve, and claim that another rival theory, the mental models theory, does not have these difficulties.

A Semantic Framework to Support the Management of Cloud-Based Service Provision Within a Global Public Inclusive Infrastructure

ABSTRACTCloud computing has opened a new era in e-commerce. As the growth and complexity of public and private clouds increases, novel solutions are needed, enabling vendors to effectively manage the cloud-based distribution of their services. To this end, this paper presents a novel semantic framework with the main aim to support providers in the overall cloud-based service provision process, in terms of service ownership, licensing, and usage monitoring. The base of our framework is an ontological schema that models service business rules definition and license agreements registration, as well as service usage monitoring, with a special focus on web service and software applications cloud-based distribution. Our proposed ontology supports complex distribution schemas, related to multiple vendor-based service distribution, as well as personalized discount schemas, facilitating the deployment of marketing strategies during cloud-based service provision. On top of this ontological schema, we have further developed service and provider reputation and trust inference mechanisms, which operate on the basis of both service usage statistics and subjective end user feedback. Taking a step forward from the state of the art, our mechanisms also provide personalized discount suggestions, further facilitating providers in defining marketing strategies. Apart from supporting providers, our mechanisms also facilitate users in service and provider selection. In order to examine the application of our framework in practice, we have developed a user interface that provides basic features of our framework, within the context of a real cloud, oriented toward personalized cloud service provision; our framework has been integrated in the “Cloud4All” Global Public Inclusive Infrastructure (GPII).

Foucault’s Perspective of Performance Appraisal in Public Administration

The main purpose of performance appraisal in organizations is, or should be, to improve the engagement, learning process and progress of the employees and to align individual with team and organizational performance. However, performance appraisal can also be interpreted as an instrument of normalization, discipline and surveillance. This study thus aims to explore and discuss the complex schema of intrinsic and extrinsic objectives of performance appraisal system of the Portuguese public organizations (SIADAP - Performance Evaluation Integrated System). We have developed an exploratory and qualitative case study to capture appraisers and appraises perceptions. The data were analyzed in light of foucauldian theories. According to the qualitative data, namely the discourses of the appraisers and their subordinates, the SIADAP is seen as an instrument of control and dominance that aims to introduce political rationalities, limiting the career progression of the employees. Though some key points of Foucault’s perspective were identified, foucauldian framework revealed some limitations to capture all the complexity inherent to performance appraisal. This study opens new perspectives about the SIADAP and can be of major importance as far as political reflection about performance appraisal in public organizations is concerned. DOI: 10.5901/mjss.2015.v6n2p375

Médecine, philosophie et révolution scientifique. Une étude bibliographique

This article examines the place that has been reserved for medicine in the historiography of the sciences. More precisely, it focuses on the motifs that have lead historians of science to grant only a minor role to medicine within the movement commonly designated by the notion of the “scientific revolution”. Among those motifs, the persistent and late application of teleological schemas in the thinking of the biological and the difficulties in “mathematizing” anatomy are often invoked. Starting with an overview of the critical literature on the topic, this bibliographical essay shows how the situation has changed over the last decades. The opposition between, on the one hand, the physical sciences founded on a model of mechanistic explanation of nature and, on the other hand, the life sciences that remained guided by a finalist mode of thinking are today much put into question. What we find today is more open reflection on the diversity of “models” for understanding the living, and on how to integrate them into more complex schemas than those that simply oppose mechanism and teleology. The essay is finally based on discussions and debates among medical doctors and philosophers in the modern period, and insists on the importance of studying this “medico-philosophical” tradition in order to avoid reconstructing a posteriori a mythical history that trends to consecrate a single model of rationality.

The Cognitive Characteristics of Mediator's Decision Making

Cognitive processes are key for understanding experts’ challenges and decisions. The prevailing stylistic dichotomy in mediation might conceal the diverse factors behind mediators’ decisions. Going beyond the dichotomy, we systematically examined the explicit and implicit challenges of mediators’ schema, the tensions emerging when a mediators’ schema meets a complex reality, and the ways mediators choose to handle their perceived challenges. To analyze these dimensions we focused on the work of eight mediators out of twenty-two mostly highly experienced mediators who came to our lab. All mediated the same simulated conflict enacted by two female disputants. All sessions were observed, videotaped and then assessed through multiple measures from 3 viewpoints: the mediators, the disputants, and three independent observers. A special effort was made to capture the cognitive aspects of the mediators and compare them with their behaviors. In the dynamic mediation interaction, mediators often reacted unconsciously and intuitively. Consistent with mediation research, mediators’ explicitly presented themselves as stylistically eclectic but most were observed behaving with little stylistic and behavioral flexibility. Mediators’ implicit schema varied from simple to complex. Two universal mechanisms of coping with the stresses of the mediation role were identified--flexibility and reflection. Greater cognitive and behavioral flexibility and reflective capacity were detected in mediators with a complex schema, and they seem to relate to higher competency and better intervention quality than those with simple schema.