Fuzzy Logic Theory and Applications in Uncertainty Management of Linguistic Evaluations for Students

Fuzzy logic can deal with information arising from perception and cognition that is uncertain, imprecise, vague, partially true, or without sharp boundaries. Fuzzy logic can be used for assigning linguistic grades and for decision making and data mining with those linguistic grades by teachers, instructors, and professors. Many aspects of fuzzy logic including fuzzy sets, linguistic variables, fuzzy rules, fuzzy math, fuzzy database queries, computational theory of perceptions, and computing with words are useful in uncertainty management of linguistic evaluations for students. This chapter provides many examples of this after describing the theory of fuzzy logic.

Parsing Clinical Trial Eligibility Criteria for Cohort Query by a Multi-Input Multi-Output Sequence Labeling Model

To enable electronic screening of eligible patients for clinical trials, free-text clinical trial eligibility criteria should be translated to a computable format. Natural language processing (NLP) techniques have the potential to automate this process. In this study, we explored a supervised multi-input multi-output (MIMO) sequence labeling model to parse eligibility criteria into combinations of fact and condition tuples. Our experiments on a small manually annotated training dataset showed that that the performance of the MIMO framework with a BERT-based encoder using all the input sequences achieved an overall lenient-level AUROC of 0.61. Although the performance is suboptimal, representing eligibility criteria into logical and semantically clear tuples can potentially make subsequent translation of these tuples into database queries more reliable.

LINGUISTIC SUMMARIES IN EVALUATING ELEMENTARY CONDITIONS AND THEIR APPLICATION IN SOFTWARE ENVIRONMENT

Data users are generally interested in two types of aggregated information: summarization of the selected attribute(s) for all considered entities and retrieval and evaluation of entities by the requirements posed on the relevant attributes. Less statistically literate users (e.g., domain experts) and the business intelligence strategic dashboards can benefit from linguistic summarization, i.e. a summary like most customers are middle-aged can be understood immediately. Evaluation of the mandatory and optional requirements of the structure P1 and most of the other posed predicates should be satisfied beneficial for analytical business intelligence dashboards and search engines in general. This work formalizes the integration of the aforementioned quantified summaries and quantified evaluation into the concept of database queries to empower their flexibility by, e.g., the nested quantified query conditions on hierarchical data structures. Later in our work, we adapted our research into practical application. We created a software environment for evaluating data based on a dataset retrieved from The Statistical Office of the Slovak republic. These datasets are aimed mainly on landscape characteristics like altitude, area sizes of towns and villages, and similar parameters. Based on user's preferences, our system recommends the most suitable place for holidays to spend on.

Translating synthetic natural language to database queries with a polyglot deep learning framework

The number of databases as well as their size and complexity is increasing. This creates a barrier to use especially for non-experts, who have to come to grips with the nature of the data, the way it has been represented in the database, and the specific query languages or user interfaces by which data are accessed. These difficulties worsen in research settings, where it is common to work with many different databases. One approach to improving this situation is to allow users to pose their queries in natural language. In this work we describe a machine learning framework, Polyglotter, that in a general way supports the mapping of natural language searches to database queries. Importantly, it does not require the creation of manually annotated data for training and therefore can be applied easily to multiple domains. The framework is polyglot in the sense that it supports multiple different database engines that are accessed with a variety of query languages, including SQL and Cypher. Furthermore Polyglotter supports multi-class queries. Good performance is achieved on both toy and real databases, as well as a human-annotated WikiSQL query set. Thus Polyglotter may help database maintainers make their resources more accessible.

Routing Communication Inside Ad Hoc Drones Network

The world knows a constant development of technology applied in different sectors of activities: health, factories, homes, transportation, and others, one of the big axes that take a lot of attention today is the drone’s field. To communicate information a fleet of drones can use different communication architectures: centralized communication architecture, satellite communication architecture, cellular network communication architecture and a specific AdHoc communication architecture called the UAANET drones architecture. In our work we focused specifically on the routing of information inside the UAANET where we analyze and compare the performances of the reactive protocol AODV and the proactive protocol OLSR, when the UAANET use an applications based on the HTTP protocol, the FTP protocol, the database queries, voice application, and video conferencing application.

DBOS

This paper lays out the rationale for building a completely new operating system (OS) stack. Rather than build on a single node OS together with separate cluster schedulers, distributed filesystems, and network managers, we argue that a distributed transactional DBMS should be the basis for a scalable cluster OS. We show herein that such a database OS (DBOS) can do scheduling, file management, and inter-process communication with competitive performance to existing systems. In addition, significantly better analytics can be provided as well as a dramatic reduction in code complexity through implementing OS services as standard database queries, while implementing low-latency transactions and high availability only once.

An adaptive spark-based framework for querying large-scale NoSQL and relational databases

The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneous data models via numerous APIs and query languages imposes challenging tasks on multi-data processing developers. Indeed, complex queries concerning homogenous data structures cannot currently be performed in a declarative manner when found in single data storage applications and therefore require additional development efforts. Many models were presented in order to address complex queries Via multistore applications. Some of these models implemented a complex unified and fast model, while others’ efficiency is not good enough to solve this type of complex database queries. This paper provides an automated, fast and easy unified architecture to solve simple and complex SQL and NoSQL queries over heterogeneous data stores (CQNS). This proposed framework can be used in cloud environments or for any big data application to automatically help developers to manage basic and complicated database queries. CQNS consists of three layers: matching selector layer, processing layer, and query execution layer. The matching selector layer is the heart of this architecture in which five of the user queries are examined if they are matched with another five queries stored in a single engine stored in the architecture library. This is achieved through a proposed algorithm that directs the query to the right SQL or NoSQL database engine. Furthermore, CQNS deal with many NoSQL Databases like MongoDB, Cassandra, Riak, CouchDB, and NOE4J databases. This paper presents a spark framework that can handle both SQL and NoSQL Databases. Four scenarios’ benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. The results show that, the CQNS achieves best latency and throughput in less time among the compared systems.

Research on Query Optimization of Classic Art Database Based on Artificial Intelligence and Edge Computing

With the changes and development of the social era, my country’s classic art is slowly being lost. In order to more effectively inherit and preserve classic art, the collection and sorting of classic art data through modern information technology has become a top priority. Database storage is a good way. However, as the amount of data grows, the requirements for computing processing power and query speed for massive amounts of data and information are also increasing day by day. Faced with this problem, this article is aimed at studying the optimization of database queries through effective algorithms to improve the efficiency of data query. Based on the traditional database query optimization algorithm, this article improves on the traditional algorithm and proposes a semi-join query optimization algorithm, which reduces the number of connection cards and the number of columns and uses the number of blocks that participate in the semi-link algorithm connection and preconnection preview and selection. And other functions reduce the size of the participating block, and the connection sent between sites reduces the cost of sending between networks. The graph data query optimization algorithm is used to optimize the graph data query in the database to reduce the extra task overhead and improve the system performance. The experimental results of this paper show that through the data query optimization algorithm of this paper, the additional task overhead is reduced by 19%, the system performance is increased by 22%, and the data query efficiency is increased by 31%.

Conversational interfaces for unconventional access to business relational data structures

PurposeCurrent possibilities of accessing business data by regular users usually involve complicated user interfaces or require technical expertise. This results in situations when business owners are separated from their data. The aim of this research is to apply an innovative approach leveraging conversational interfaces to tackle this problem.Design/methodology/approachThe authors examine the current possibilities of accessing business data by business, users with an emphasis on conversational interfaces employing a chatbot as an alternative to traditional approaches. The authors propose a new concept relying on a guided conversation, and through experiments with a real chatbot and database, the authors demonstrate the benefits of the proposed approach.FindingsThe authors found out that the key to the success of our approach is a decomposition of complex database queries and their incremental construction in conversations. This also enables natural discovery of the domain model through constantly provided feedback. Based on the experiments with a real chatbot, the authors demonstrate that defining conversation flows and maintaining the conversation context is a crucial aspect contributing to the overall accuracy, together with keeping the conversation within the defined limits in its certain parts.Originality/valueThe authors present a novel approach using natural language interfaces for accessing data by business users. In contrast to existing approaches, the authors emphasize incremental construction of queries, predefined conversation flows and constraining the conversations, when necessary.