Relational Query Research Articles

Patterns of Google search behavior for epilepsy and seizures in the Philippines: An infodemiological study

BackgroundThe internet has been established as a rich source of health information that is well utilized by the public. Online health information-seeking behavior may reflect public awareness in particular health-related topics, including epilepsy and seizures. ObjectiveThis study aimed to describe online search behavior for epilepsy and seizures in the Philippines through the examination of trends of Philippine-based Google search queries for epilepsy and seizure-related terms. MethodsThe search volume indices (SVIs) for the keywords “epilepsy (disorder)” and “seizure (disease)” based on Philippine-based Google searches from January 2004 to July 2021 were generated using Google Trends. Descriptive analysis of search volume patterns, including related topics and queries, was performed. ResultsThere was a recent relative increase in Philippine-based searches related to the term “seizure” with a concurrent relative decline in searches related to the term “epilepsy” over time. The Philippines had the fifth-highest SVI for “seizure” based on worldwide trends. Most searches centered on the definition, symptomatology, etiologies, classification, and treatment of seizures and epilepsy. Queries containing seizure-related terms rose in volume by more than 5000% during the seventeen-year period. Search volume tended to peak during the National Epilepsy Awareness Week in September but slumped during holiday months. ConclusionsOnline search behavior in the Philippines for seizure-related terms appears to be on the rise and may reflect increasing public awareness in epilepsy. Factors including fears surrounding epileptic seizures, the stigma surrounding the term “epilepsy”, and seasonal events including a regularly held epilepsy awareness week may influence online search behavior.

The Federal Menu Labeling Law and Twitter Discussions about Calories in the United States: An Interrupted Time-Series Analysis.

Public awareness of calories in food sold in retail establishments is a primary objective of the menu labeling law. This study explores the extent to which we can use social media and internet search queries to understand whether the federal calorie labeling law increased awareness of calories. To evaluate the association of the federal menu labeling law with tweeting about calories we retrieved tweets that contained the term “calorie(s)” from the CompEpi Geo Twitter Database from 1 January through 31 December in 2016 and 2018. Within the same time period, we also retrieved time-series data for search queries related to calories via Google Trends (GT). Interrupted time-series analysis was used to test whether the federal menu labeling law was associated with a change in mentions of “calorie(s)” on Twitter and relative search queries to calories on GT. Before the implementation of the federal calorie labeling law on 7 May 2018, there was a significant decrease in the baseline trend of 4.37 × 10−8 (SE = 1.25 × 10−8, p < 0.001) mean daily ratio of calorie(s) tweets. A significant increase in post-implementation slope of 3.19 × 10−8 (SE = 1.34 × 10−8 , p < 0.018) mean daily ratio of calorie(s) tweets was seen compared to the pre-implementation slope. An interrupted time-series (ITS) analysis showed a small, statistically significant upward trend of 0.0043 (SE = 0.036, p < 0.001) weekly search queries for calories pre-implementation, with no significant level change post-implementation. There was a decrease in trend of 1.22 (SE = 0.27, p < 0.001) in search queries for calories post-implementation. The federal calorie labeling law was associated with a 173% relative increase in the trend of mean daily ratio of tweets and a -28381% relative change in trend for search queries for calories. Twitter results demonstrate an increase in awareness of calories because of the addition of menu labels. Google Trends results imply that fewer people are searching for the calorie content of their meal, which may no longer be needed since calorie information is provided at point of purchase. Given our findings, discussions online about calories may provide a signal of an increased awareness in the implementation of calorie labels.

Modularis

The enormous quantity of data produced every day together with advances in data analytics has led to a proliferation of data management and analysis systems. Typically, these systems are built around highly specialized monolithic operators optimized for the underlying hardware. While effective in the short term, such an approach makes the operators cumbersome to port and adapt, which is increasingly required due to the speed at which algorithms and hardware evolve. To address this limitation, we present Modularis , an execution layer for data analytics based on sub-operators , i.e., composable building blocks resembling traditional database operators but at a finer granularity. To demonstrate the feasibility and advantages of our approach, we use Modularis to build a distributed query processing system supporting relational queries running on an RDMA cluster, a serverless cloud platform, and a smart storage engine. Modularis requires minimal code changes to execute queries across these three diverse hardware platforms, showing that the sub-operator approach reduces the amount and complexity of the code to maintain. In fact, changes in the platform affect only those sub-operators that depend on the underlying hardware (in our use cases, mainly the sub-operators related to network communication). We show the end-to-end performance of Modularis by comparing it with a framework for SQL processing (Presto), a commercial cluster database (SingleStore), as well as Query-as-a-Service systems (Athena, BigQuery). Modularis outperforms all these systems, proving that the design and architectural advantages of a modular design can be achieved without degrading performance. We also compare Modularis with a hand-optimized implementation of a join for RDMA clusters. We show that Modularis has the advantage of being easily extensible to a wider range of join variants and group by queries, all of which are not supported in the hand-tuned join.

Chat-bot: An Information Retrieving for Myocardial Infarction and Diabetes

Medical information is being very important type for clinicians. We aim to build clinical chat-bot which holds information about diseases namely diabetes and heart diseases. Chat- bots are designed to make people feel they are interacting to a human for their related queries rather to believe for machine feelings. These chat-bot are computer program coded to build textual and conversation which are intelligent and gives us logical replies which are already learned. This research focuses to design such type of bot which act on two said major diseases so the working chat-bot will provide interaction between machine and man where machine will function as a virtual doctor here chat-bot will play a leading role by working as a virtual assistant and skipping the gap between patients and clinicians.

Database Principles and Challenges in Text Analysis

A common conceptual view of text analysis is that of a two-step process, where we first extract relations from text documents and then apply a relational query over the result. Hence, text analysis shares technical challenges with, and can draw ideas from, relational databases. A framework that formally instantiates this connection is that of the document spanners. In this article, we review recent advances in various research efforts that adapt fundamental database concepts to text analysis through the lens of document spanners. Among others, we discuss aspects of query evaluation, aggregate queries, provenance, and distributed query planning.

Big Data-Driven Pedestrian Analytics: Unsupervised Clustering and Relational Query Based on Tencent Street View Photographs

Recent technological advancements in geomatics and mobile sensing have led to various urban big data, such as Tencent street view (TSV) photographs; yet, the urban objects in the big dataset have hitherto been inadequately exploited. This paper aims to propose a pedestrian analytics approach named vectors of uncountable and countable objects for clustering and analysis (VUCCA) for processing 530,000 TSV photographs of Hong Kong Island. First, VUCCA transductively adopts two pre-trained deep models to TSV photographs for extracting pedestrians and surrounding pixels into generalizable semantic vectors of features, including uncountable objects such as vegetation, sky, paved pedestrian path, and guardrail and countable objects such as cars, trucks, pedestrians, city animals, and traffic lights. Then, the extracted pedestrians are semantically clustered using the vectors, e.g., for understanding where they usually stand. Third, pedestrians are semantically indexed using relations and activities (e.g., walking behind a guardrail, road-crossing, carrying a backpack, or walking a pet) for queries of unstructured photographic instances or natural language clauses. The experiment results showed that the pedestrians detected in the TSV photographs were successfully clustered into meaningful groups and indexed by the semantic vectors. The presented VUCCA can enrich eye-level urban features into computational semantic vectors for pedestrians to enable smart city research in urban geography, urban planning, real estate, transportation, conservation, and other disciplines.

Effective and efficient skyline query processing over attribute-order-preserving-free encrypted data in cloud-enabled databases

Making co-existent and convergent the need for efficiency of relational query processing over Clouds and the security of data themselves is figuring-out how one of the most challenging research problems in the Big Data era. Indeed, in actual analytics-oriented engines, such as Google Analytics and Amazon S3, where key–value storage-representation and efficient-management models are employed as to cope with the simultaneous processing of billions of transactions, querying encrypted data is becoming one of the most annoying problem, which has also attracted a great deal of attention from the research community. While this issue has been applied to a large variety of data formats, e.g. relational, RDF and multidimensional data, very few initiatives have pointed-out skyline query processing over encrypted data, which is, indeed, relevant for database analytics. In order to fulfill this methodological and technological gap, in this paper we introduce an innovative algorithm for effectively and efficiently supporting skyline query processing over encrypted data in Cloud-enabled databases, named as Attribute-Order-Preserving-Free-SFS (AOPF-SFS), a suitable extension of the well-known Sort-Filter-Skyline (SFS) algorithm. The proposed algorithm enables the processing of skyline queries over encrypted data, even without preserving the order on each attribute as order-preserving encryption would do. We also present eSkyline, a prototype system that embeds AOPF-SFS equipped with a suitable query interface comprising an encryption scheme that facilitates the evaluation of domination relationships, hence allows for state-of-the-art skyline processing algorithms to be used. In order to prove the effectiveness and the reliability of our system, we also provide the details of the underlying encryption scheme, plus a suitable GUI that allows a user to interact with a server, and showcases the efficiency of computing skyline queries and decrypting the results.

ROLE OF AI-BASED VIRTUAL ASSISTANTS IN ORAL AND MAXILLOFACIAL RADIOLOGY REPORT WRITING

<h3>BACKGROUND</h3> Artificial Intelligence (AI) encompasses an array of devices, solutions, and codes ranging from a simple calculator to complex machine learning devices. A simple and successful AI system that is being routinely used is a voice-activated virtual assistant. There are several such devices in the market, including Alexa, Siri, Cortana, and Google Assistant. These devices offer a very simple voice-activated front end powered by a database at the back end. There are several instances where radiologists refer to a textbook or visit a website to look for disease-specific features, genetic mutations, and differential diagnoses. <h3>OBJECTIVE(S)</h3> The objective of this study was to evaluate the role and status of 4 voice-based virtual assistants in responding to radiology related queries. <h3>STUDY DESIGN</h3> A questionnaire consisting of 100 questions was queried to 4 commercially available voice-based virtual assistants: Alexa, Siri, Cortana, and Google Assistant. The questions were categorized based on 5 categories. The categorization was based on the frequency and reason for a radiologist to refer either to a textbook or an online resource before diagnosing and finalizing a radiology report. The evaluators queried the devices and rated them on a 4-point modified Likert scale: (1) The device responded with adequate information, (2) The device responded but did not provide adequate information, (3) The device did not know the response to the question, and (4) The device recognized but did not provide adequate information and led to a series of web pages. <h3>RESULTS</h3> The order of efficiency was Google Assistant, Cortana, Siri, and Alexa. A significant difference between the examiners was observed with Cortana in anatomy, dental anatomy, differential diagnosis, and pathology. There was a significant difference between the examiners with Google Assistant for queries about medication. <h3>DISCUSSION/CONCLUSIONS</h3> There is a significant gap in retrieving simple to multilevel queries on oral radiology topics for all of the virtual assistants evaluated in this study.

The Impact of COVID-19 on Conspiracy Hypotheses and Risk Perception in Italy: Infodemiological Survey Study Using Google Trends.

BackgroundCOVID-19 has caused the worst international crisis since World War II. Italy was one of the countries most affected by both the pandemic and the related infodemic. The success of anti–COVID-19 strategies and future public health policies in Italy cannot separate itself from the containment of fake news and the divulgation of correct information.ObjectiveThe aim of this paper was to analyze the impact of COVID-19 on web interest in conspiracy hypotheses and risk perception of Italian web users.MethodsGoogle Trends was used to monitor users’ web interest in specific topics, such as conspiracy hypotheses, vaccine side effects, and pollution and climate change. The keywords adopted to represent these topics were mined from Bufale.net—an Italian website specializing in detecting online hoaxes—and Google Trends suggestions (ie, related topics and related queries). Relative search volumes (RSVs) of the time-lapse periods of 2016-2020 (pre–COVID-19) and 2020-2021 (post–COVID-19) were compared through percentage difference (∆%) and the Welch t test (t). When data series were not stationary, other ad hoc criteria were used. The trend slopes were assessed through Sen slope (SS). The significance thresholds have been indicatively set at P=.05 and t=1.9.ResultsThe COVID-19 pandemic drastically increased Italian netizens’ interest in conspiracies (∆% ∈ [60, 288], t ∈ [6, 12]). Web interest in conspiracy-related queries across Italian regions increased and became more homogeneous compared to the pre–COVID-19 period (average RSV=80±2.8, tmin=1.8, ∆min%=+12.4, min∆SD%=–25.8). In addition, a growing trend in web interest in the infodemic YouTube channel ByoBlu has been highlighted. Web interest in hoaxes has increased more than interest in antihoax services (t1=11.3 vs t2=4.5; Δ1%=+157.6 vs Δ2%=+84.7). Equivalently, web interest in vaccine side effects exceeded interest in pollution and climate change (SSvaccines=0.22, P<.001 vs SSpollution=0.05, P<.001; ∆%=+296.4). To date, a significant amount of fake news related to COVID-19 vaccines, unproven remedies, and origin has continued to circulate. In particular, the creation of SARS-CoV-2 in a Chinese laboratory constituted about 0.04% of the entire web interest in the pandemic.ConclusionsCOVID-19 has given a significant boost to web interest in conspiracy hypotheses and has made it more uniform across regions in Italy. The pandemic accelerated an already-growing trend in users’ interest toward some fake news sources, including the 500,000-subscriber YouTube channel ByoBlu, which was removed from the platform by YouTube for disinformation in March 2021. The risk perception related to COVID-19 vaccines has been so distorted that vaccine side effect–related queries outweighed those relating to pollution and climate change, which are much more urgent issues. Moreover, a large amount of fake news has circulated about COVID-19 vaccines, remedies, and origin. Based on these findings, it is recommended that the Italian authorities implement more effective infoveillance systems, and that communication by the mass media be less sensationalistic and more consistent with the available scientific evidence. In this context, Google Trends can be used to monitor users’ response to specific infodemiological countermeasures. Further research is needed to understand the psychological mechanisms that regulate risk perception.

Measuring Public Concern About COVID-19 in Japanese Internet Users Through Search Queries: Infodemiological Study.

BackgroundCOVID-19 has disrupted lives and livelihoods and caused widespread panic worldwide. Emerging reports suggest that people living in rural areas in some countries are more susceptible to COVID-19. However, there is a lack of quantitative evidence that can shed light on whether residents of rural areas are more concerned about COVID-19 than residents of urban areas.ObjectiveThis infodemiology study investigated attitudes toward COVID-19 in different Japanese prefectures by aggregating and analyzing Yahoo! JAPAN search queries.MethodsWe measured COVID-19 concerns in each Japanese prefecture by aggregating search counts of COVID-19–related queries of Yahoo! JAPAN users and data related to COVID-19 cases. We then defined two indices—the localized concern index (LCI) and localized concern index by patient percentage (LCIPP)—to quantitatively represent the degree of concern. To investigate the impact of emergency declarations on people's concerns, we divided our study period into three phases according to the timing of the state of emergency in Japan: before, during, and after. In addition, we evaluated the relationship between the LCI and LCIPP in different prefectures by correlating them with prefecture-level indicators of urbanization.ResultsOur results demonstrated that the concerns about COVID-19 in the prefectures changed in accordance with the declaration of the state of emergency. The correlation analyses also indicated that the differentiated types of public concern measured by the LCI and LCIPP reflect the prefectures’ level of urbanization to a certain extent (ie, the LCI appears to be more suitable for quantifying COVID-19 concern in urban areas, while the LCIPP seems to be more appropriate for rural areas).ConclusionsWe quantitatively defined Japanese Yahoo users’ concerns about COVID-19 by using the search counts of COVID-19–related search queries. Our results also showed that the LCI and LCIPP have external validity.

Searching for scientific evidence in a pandemic: An overview of TREC-COVID

We present an overview of the TREC-COVID Challenge, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19. The goals of TREC-COVID include the construction of a pandemic search test collection and the evaluation of IR methods for COVID-19. The challenge was conducted over five rounds from April to July 2020, with participation from 92 unique teams and 556 individual submissions. A total of 50 topics (sets of related queries) were used in the evaluation, starting at 30 topics for Round 1 and adding 5 new topics per round to target emerging topics at that state of the still-emerging pandemic. This paper provides a comprehensive overview of the structure and results of TREC-COVID. Specifically, the paper provides details on the background, task structure, topic structure, corpus, participation, pooling, assessment, judgments, results, top-performing systems, lessons learned, and benchmark datasets.

TraNCE

Nested relational query languages have long been seen as an attractive tool for scenarios involving large hierarchical datasets. There has been a resurgence of interest in nested relational languages. One driver has been the affinity of these languages for large-scale processing platforms such as Spark and Flink. This demonstration gives a tour of TraNCE, a new system for processing nested data on top of distributed processing systems. The core innovation of the system is a compiler that processes nested relational queries in a series of transformations; these include variants of two prior techniques, shredding and unnesting, as well as a materialization transformation that customizes the way levels of the nested output are generated. The TraNCE platform builds on these techniques by adding components for users to create and visualize queries, as well as data exploration and notebook execution targets to facilitate the construction of large-scale data science applications. The demonstration will both showcase the system from the viewpoint of usability by data scientists and illustrate the data management techniques employed.

Potential Impact of a Paper About COVID-19 and Smoking on Twitter Users' Attitudes Toward Smoking: Observational Study.

BackgroundA cross-sectional study (Miyara et al, 2020) conducted by French researchers showed that the rate of current daily smoking was significantly lower in patients with COVID-19 than in the French general population, implying a potentially protective effect of smoking.ObjectiveWe aimed to examine the dissemination of the Miyara et al study among Twitter users and whether a shift in their attitudes toward smoking occurred after its publication as preprint on April 21, 2020.MethodsTwitter posts were crawled between April 14 and May 4, 2020, by the Tweepy stream application programming interface, using a COVID-19–related keyword query. After filtering, the final 1929 tweets were classified into three groups: (1) tweets that were not related to the Miyara et al study before it was published, (2) tweets that were not related to Miyara et al study after it was published, and (3) tweets that were related to Miyara et al study after it was published. The attitudes toward smoking, as expressed in the tweets, were compared among the above three groups using multinomial logistic regression models in the statistical analysis software R (The R Foundation).ResultsTemporal analysis showed a peak in the number of tweets discussing the results from the Miyara et al study right after its publication. Multinomial logistic regression models on sentiment scores showed that the proportion of negative attitudes toward smoking in tweets related to the Miyara et al study after it was published (17.07%) was significantly lower than the proportion in tweets that were not related to the Miyara et al study, either before (44/126, 34.9%; P<.001) or after the Miyara et al study was published (68/198, 34.3%; P<.001).ConclusionsThe public’s attitude toward smoking shifted in a positive direction after the Miyara et al study found a lower incidence of COVID-19 cases among daily smokers.

Data dependencies for query optimization: a survey

Effective query optimization is a core feature of any database management system. While most query optimization techniques make use of simple metadata, such as cardinalities and other basic statistics, other optimization techniques are based on more advanced metadata including data dependencies, such as functional, uniqueness, order, or inclusion dependencies. This survey provides an overview, intuitive descriptions, and classifications of query optimization and execution strategies that are enabled by data dependencies. We consider the most popular types of data dependencies and focus on optimization strategies that target the optimization of relational database queries. The survey supports database vendors to identify optimization opportunities as well as DBMS researchers to find related work and open research questions.

Reducing Broad-Spectrum Antibiotic Treatment of Simple Group A Streptococcal Infections to Reduce Harm to the Microbiome.

BackgroundBroad-spectrum antibiotics disrupt the human microbiome resulting in a greater risk of harmful, long-term conditions that impact human health. Group A streptococcal (GAS) infections can be treated with penicillin.Objective We examined the treatment of simple GAS infections to assess the use of broad-spectrum antibiotics.Methods Smart relational database extraction queries from January 1, 2016 to July 10, 2019 (3.6 years) of patients less than 22 years old in a 4-hospital system electronic medical record (EMR).Results We found 1778 non-ED outpatients and 873 ED patients with simple GAS infections who were not allergic to penicillin. A total of 75% and 44% of non-ED and ED patients were treated with broad-spectrum antibiotics, respectively (p < 0.001). Older patients were treated with penicillin alone more frequently than younger age groups (p < 0.001).Conclusion These findings highlight opportunities for clinicians to reduce the utilization of broad-spectrum antibiotics for the treatment of simple GAS infections to reduce harm to the microbiome.

Tidy Tuples and Flying Start: fast compilation and fast execution of relational queries in Umbra

Although compiling queries to efficient machine code has become a common approach for query execution, a number of newly created database system projects still refrain from using compilation. It is sometimes claimed that the intricacies of code generation make compilation-based engines too complex. Also, a major barrier for adoption, especially for interactive ad hoc queries, is long compilation time. In this paper, we examine all stages of compiling query execution engines and show how to reduce compilation overhead. We incorporate the lessons learned from a decade of generating code in HyPer into a design that manages complexity and yields high speed. First, we introduce a code generation framework that establishes abstractions to manage complexity, yet generates code in a single fast pass. Second, we present a program representation whose data structures are tuned to support fast code generation and compilation. Third, we introduce a new compiler backend that is optimized for minimal compile time, and simultaneously, yields superior execution performance to competing approaches, e.g., Volcano-style or bytecode interpretation. We implemented these optimizations in our database system Umbra to show that it is possible to unite fast compilation and fast execution. Indeed, Umbra achieves unprecedentedly low query latencies. On small data sets, it is even faster than interpreter engines like DuckDB and PostgreSQL. At the same time, on large data sets, its throughput is on par with the state-of-the-art compiling system HyPer.

Old age liaison psychiatry: audit assessing adherence to referral pathway and referral characteristics including indications, interventions and outcomes

AimsThis audit covered 3 hospitals in Glasgow City which has 1221 beds providing inpatient healthcare for the north east region of the city. To improve the referral process,we aimed to verify adherence to existing referral pathway and adequacy of information provided by referrals. Referral characteristics including referral indication, intervention and outcomes were accounted for to identify area interest that may help improve the referral process.MethodOur referral pathway involves completion of a Microsoft Word referral template subsequently sent electronically to an internal electronic mail.Referrals in a 2 month period were included in the audit. Each referral was reviewed for adherence to the referral template, adequacy of provided information and referral indications. Intervention in the form of staff input, Mental Health Act status, psychotropic medication prescribed and given diagnosis was ascertained via staff electronic entry records.Result139 referrals were included. 114 referrals (82%) adhered to the referral template. 72 referrals (52%) contained adequate information. Common referral indications were delirium (23%), agitation (20%), low mood (18%) and cognitive decline queries (18%). Staff input ranged from psychiatrist input (46%), liaison nurses (40%), clinical psychology (1%) and shared input (13%). 16 referrals (12%) resulted in subsequent detention under the Mental Health Act. Psychotropic medications prior to liaison assessment included antidepressants (49%), antipsychotics (29%) and benzodiazepines (16%). Liaison assessment resulted in increase use of antipsychotic (55%) and reduction of antidepressants (29%) and benzodiazepines (10%), Delirium (34%), dementia (21%), Mood &amp; Anxiety related disorders (18%) and Query of Cognitive Impairment (14%) were recorded as the most discussed diagnosis.ConclusionReferrals with inadequate details affect the service's ability to efficiently assess for clinical urgency and matching of appropriate interventions to suit clinical needs. The percentage difference in delirium between referral indication and diagnosis highlights that delirium can be under-recognised, resulting in potentially delayed treatment. Identifying common given diagnosis and differences in psychotropic medication prescribing pattern points to the need for training and support of acute medical ward staff in utilising therapeutics for management of acute mental health disorder.A pending electronic referral pathway with mandatory entries and linked relevant online resources can encourage early recognition of acute mental health disorder and prompt early management including the use of appropriate therapeutics. An additional feature allowing direct referrals by acute ward staff to community mental health team would support continuity of care for discharged patients needing ongoing mental health assessment.

Erectile dysfunction and prostate diseases are the predominant Google search terms amongst men's health topics.

Patients are becoming increasingly active consumers of health information on the internet with urologic concerns being no exception. Our objective was to explore online search trends for topics related to men's health and identify information-seeking patterns related to news and media coverage of these topics. We used Google Trends ( http://google.com/trends ) to explore search trends for various search terms related to men's health in the United States over a 5-year period. Search queries provided graphs depicting search volume as a function of time, geographical data, and related topics and queries. Isolated spikes in search volume were further explored to identify a related event. Erectile dysfunction was the most-searched topic over the last 5 years in the United States. Prostate cancer and benign prostatic hyperplasia were the second and third most-searched topics, respectively. Other popular topics involved symptoms or pathologies of the testicles and penis. Most topics had relatively stable search volumes, with the exceptions of premature ejaculation and Peyronie's disease. Several observed spikes in search volume were attributable to singular events, mostly in the form of online article publications or social media posts. We believe it may be helpful for providers to stay informed of cultural events relating to medical conditions to anticipate patient concerns.

GENSYNTH: Synthesizing Datalog Programs without Language Bias

Techniques for learning logic programs from data typically rely on language bias mechanisms to restrict the hypothesis space. These methods are therefore limited by the user's ability to tune them such that the hypothesis space is simultaneously large enough to include the target program but small enough to admit a tractable search. We propose a technique to learn Datalog programs from input-output examples without requiring the user to specify any language bias. It employs an evolutionary search strategy that mutates candidate programs and evaluates their fitness on the examples using an off-the-shelf Datalog interpreter. We have implemented our approach in a tool called GenSynth and evaluate it on diverse tasks from knowledge discovery, program analysis, and relational queries. Our experiments show that GenSynth can learn correct programs from few examples, including for tasks that require recursion and invented predicates, and is robust to noise.

“Stay at home with bakery products” can be public motto of quarantine days in the early period of COVID-19 outbreak: A nutritional infodemiology study

AimsThe aim of this retrospective infodemiological study is to investigate the public interest to the bakery products in the early period of coronavirus outbreak through Google Trends and open access data banks. MethodsThe category and regions were selected “food and drink”, Turkey, Italy and Sweden, respectively. Keywords were searched in Turkish, Italian and Swedish language by Google Trends. Search spectrums were “1 February 2011-1 September 2020” and “1 February-1 September 2020” to determine the monthly and daily search queries, successively. Sweden was included into study as a control group because there was no strict implementation in the pandemic. Daily case number of coronavirus, stringency index, stay at home requirements value and relative search volume (RSV) scores about selected foods and coronavirus were synthesized. ResultsSearch queries of “flour”, “yeast”, “dough” and “bread” were too higher in Turkey and Italy within the outbreak period, corresponding to previous years. The RSV score of bread was higher than vegetable, fruit, fish and meat in all countries, but search profiles were different. It has been determined the potential relationship between RSV scores about some bakery products and stringency index in Turkey and Italy. In addition, daily new case number of coronavirus and RSV score of coronavirus can be related to public interests to bakery products in Italy. Bakery products related top queries were usually about finding a recipe. ConclusionQuarantine enforcement can cause the increased web-based search volume of the bakery products in the early period of COVID-19 outbreak. Understanding of search queries about food choices will help to effectively combat against coronavirus. According to followed data, the public should be informed about the outcomes of dietary habits in the quarantine regions.