Relational Query Research Articles

Toothpaste-related interests of Google users from different countries.

The Internet is a growing source of knowledge and can provide information about oral health. This ecological study aimed to characterize the interests in toothpaste among Google users from different countries. Our hypothesis was that there would be an increase in Google users' interest in information about toothpaste. This retrospective longitudinal ecological study analyzed the toothpaste-related interest of Google users from 10 countries between January 2004 and December 2020. The monthly variation in relative search volume (RSV) and the main related queries were determined using Google Trends. Autoregressive integrated moving average (ARIMA) forecasting models were built to establish the predictive RSV values for toothpaste for an additional 12 months. Autocorrelation plots and the generalized additive model (GAM) were used to diagnose trends and seasonality in RSV curves. Additionally, the influence of social isolation related to the outbreak of COVID-19 was analyzed. Although not detected by autocorrelation function (ACF) and partial autocorrelation function (PACF) analyses, the heuristic analysis showed an increase in the interest in toothpaste-related information in all countries, with a stable trend observed in the 12-month forecasts, except for the increases in the United Kingdom and South Africa. Also, GAM analyses demonstrated a non-significant monthly or quarterly seasonal influence on data. In addition, social isolation during the COVID-19 pandemic did not influence the online information-seeking behavior of Google Search users linked to this topic. We confirmed the hypothesis that the interest of Google Search users in information about toothpaste increased in all of the 10 assessed countries.

Quality evaluation of digital voice assistants for diabetes management

<abstract><sec> <title>Background</title> <p>Digital voice assistants (DVAs) are increasingly used to search for health information. However, the quality of information provided by DVAs is not consistent across health conditions. From our knowledge, there have been no studies that evaluated the quality of DVAs in response to diabetes-related queries. The objective of this study was to evaluate the quality of DVAs in relation to queries on diabetes management.</p> </sec><sec> <title>Materials and methods</title> <p>Seventy-four questions were posed to smartphone (Apple Siri, Google Assistant, Samsung Bixby) and non-smartphone DVAs (Amazon Alexa, Sulli the Diabetes Guru, Google Nest Mini, Microsoft Cortana), and their responses were compared to that of Internet Google Search. Questions were categorized under diagnosis, screening, management, treatment and complications of diabetes, and the impacts of COVID-19 on diabetes. The DVAs were evaluated on their technical ability, user-friendliness, reliability, comprehensiveness and accuracy of their responses. Data was analyzed using the Kruskal-Wallis and Wilcoxon rank-sum tests. Intraclass correlation coefficient was used to report inter-rater reliability.</p> </sec><sec> <title>Results</title> <p>Google Assistant (n = 69/74, 93.2%), Siri and Nest Mini (n = 64/74, 86.5% each) had the highest proportions of successful and relevant responses, in contrast to Cortana (n = 23/74, 31.1%) and Sulli (n = 10/74, 13.5%), which had the lowest successful and relevant responses. Median total scores of the smartphone DVAs (Bixby 75.3%, Google Assistant 73.3%, Siri 72.0%) were comparable to that of Google Search (70.0%, p = 0.034), while median total scores of non-smartphone DVAs (Nest Mini 56.9%, Alexa 52.9%, Cortana 52.5% and Sulli the Diabetes Guru 48.6%) were significantly lower (p < 0.001). Non-smartphone DVAs had much lower median comprehensiveness (16.7% versus 100.0%, p < 0.001) and reliability scores (30.8% versus 61.5%, p < 0.001) compared to Google Search.</p> </sec><sec> <title>Conclusions</title> <p>Google Assistant, Siri and Bixby were the best-performing DVAs for answering diabetes-related queries. However, the lack of successful and relevant responses by Bixby may frustrate users, especially if they have COVID-19 related queries. All DVAs scored highly for user-friendliness, but can be improved in terms of accuracy, comprehensiveness and reliability. DVA designers are encouraged to consider features related to accuracy, comprehensiveness, reliability and user-friendliness when developing their products, so as to enhance the quality of DVAs for medical purposes, such as diabetes management.</p> </sec></abstract>

Becoming Human: Matter and Meaning in an Antiblack World

Becoming Human: Matter and Meaning in an Antiblack World

An infodemiological study of worldwide google search volumes for major depressive disorder and persistent depressive disorder from 2004 to 2021

BackgroundMental health promotion programs have included online strategies in their repertoire. Likewise, there has been an increasing utilization of online mental health information. Thus, there may be a need to explore the utilization of online information for depressive disorders. In doing so, it can reveal public expectations, needs, and online venues that can be used for depression information programs. MethodsThis infodemiological study explored the worldwide searches for major depressive disorder and persistent depressive disorder using relative search volumes (RSV) and top and rising related queries and topics from Google Trends. RSV is a value that represents the frequency or volume of searches normalized against the largest search volume in the study period and geography. Top and rising search-related queries and topics are terms and topics that online users also input into the Google search engine related to the keyword of interest. After describing the data, future RSVs were predicted using ARIMA. ResultsThe findings revealed that searches for depressive disorders decreased and may further decrease in succeeding years. It also revealed increasing searches for diagnostics criteria, including DSM-5. There was also increased public interest in portraying depression. It also found a digital divide in searches for depressive disorders, where high-income countries searched for more online information for depressive disorders. ConclusionThis study findings’ highlights the needs and opportunities in online information for depressive disorders. Nonetheless, further studies are needed to understand the utilization and effects of online information about depressive disorders in improving mental health promotion and literacy.

Price Discovery Mechanism in Unified Market Platform (UMP): A Case Study of APMC Raichur

The study attempted to analyze how prices were discovered under the e-tending method in the Raichur market, Karnataka, during 2017-18. Correlation, multiple regression and Gini concentration ratio were used to analyse data.The major findings of the study include the e-tendering system under UMP had considerable potential to infuse competition and fetch better prices for farmers; the pricing mechanism of paddy showed there was a clear indication if one bid increases for a lot, there was a 13.8 per cent increase in the price, the numbers of bids per lot and lot size had a positive effect; moisture content had a negative impact on the price of paddy. The study suggested the policy options were: market performance has improved under UMP, so the scope for expansion of e-trading in other markets in Karnataka as well as India, there is a need to create awareness among farmers about to follow quality parameters according to UMP guidelines, and Government needs to strengthen the infrastructure for assaying of agricultural commodities at farm level or community level; Strengthen the IT infrastructure for resolving all UMP related queries of stakeholders in a single window.

A Relative Information Gain-based Query Performance Prediction Framework with Generated Query Variants

Query performance prediction (QPP) methods, which aim to predict the performance of a query, often rely on evidences in the form of different characteristic patterns in the distribution of Retrieval Status Values (RSVs). However, for neural IR models, it is usually observed that the RSVs are often less reliable for QPP because they are bounded within short intervals, different from the situation for statistical models. To address this limitation, we propose a model-agnostic QPP framework that gathers additional evidences by leveraging information from the characteristic patterns of RSV distributions computed over a set of automatically generated query variants, relative to that of the current query. Specifically, the idea behind our proposed method—Weighted Relative Information Gain (WRIG), is that a substantial relative decrease or increase in the standard deviation of the RSVs of the query variants is likely to be a relative indicator of how easy or difficult the original query is. To cater for the absence of human-annotated query variants in real-world scenarios, we further propose an automatic query variant generation method. This can produce variants in a controlled manner by substituting terms from the original query with new ones sampled from a weighted distribution, constructed either via a relevance model or with the help of an embedded representation of query terms. Our experiments on the TREC-Robust, ClueWeb09B, and MS MARCO datasets show that WRIG, by the use of this relative changes in QPP estimate, leads to significantly better results than a state-of-the-art baseline method that leverages information from (manually created) query variants by the application of additive smoothing [ 64 ]. The results also show that our approach can improve the QPP effectiveness of neural retrieval approaches in particular.

On neighborhood inverse sum indeg index of molecular graphs with chemical significance

Chemical graph theory is an interdisciplinary field that analyses the molecular structure of a chemical compound as a graph and investigates related mathematical queries by employing graph theoretical and computational techniques. The topological index is an important tool in this area that associates a numerical value with a graph structure. It can be interpreted as a real-valued function that represents the physico-chemical information of a chemical compound. One of the most recently reported neighbourhood degree sum-based indices is the neighbourhood inverse sum indeg index (NI). In this work, we first investigate the application potential of the NI index by exploring its predictive potential and isomer discrimination ability. Following that, some fascinating mathematical features of NI are revealed. Extremal values of NI are estimated for the class of all trees and unicyclic graphs. Furthermore, some crucial upper bounds on NI are set up in terms of well-known graph parameters, including graph order,independence number, and vertex connectivity. Associations between NI and existing indices are also demonstrated.

Opinion classification at subtopic level from COVID vaccination-related tweets.

Coronavirus disease 2019 (Covid-19) is a contiguous disease which affected a large volume of population with a high mortality rate across the globe. For dealing with the recent spread of COVID-19, one of the prime measures was to vaccinate people in full extent. People across the globe have diverse opinion regarding the vaccination process, its side effect and effectiveness. Such opinions get located into different micro-blogging sites including twitter. Opinion mining through analyzing public sentiments of such micro-blogs is a common method for detection of public responses. This paper focuses on classifying the public opinions expressed related to COVID-19 vaccination at sub topic level. The procedure tries to find out different keywords regarding positive, negative and neutral sentences. From those keywords, different related query set was constructed using Rocchio query expansion algorithm for positive, negative and neutral sentiments. Later Extended query set is used to form subtopic using LDA algorithm to identify the nature of the tweets. The proposed LDA model came across with 0.56 coherence score with twenty subtopics, which is fair enough to classify the tweets in different classes. This trained model is finally used to classify the tweets in real time with Apache Kafka framework regarding different subtopic based on positive, negative or neutral sentiment.

Solving the SPARQL query containment problem with SpeCS

The query containment problem is a fundamental computer science problem which was originally defined for relational queries. With the growing popularity of the sparql query language, it became relevant and important in this new context: reliable and efficient sparql query containment solvers may have various applications within static analysis of queries, especially in the area of query optimizations and refactoring. In this paper, we present a new approach for solving the query containment problem in sparql. The approach is based on reducing the query containment problem to the satisfiability problem in first order logic. It covers a wide range of the sparql language constructs, including union of conjunctive queries, blank nodes, projections, subqueries, clauses from, filter, optional, graph, etc. It also covers containment under rdf schema entailment regime, and it can deal with the subsumption relation. We describe an implementation of the approach, an open source solver SpeCS and its thorough experimental evaluation on two relevant benchmarks, Query Containment Benchmark and SQCFramework. As a side result, SpeCS identified incorrect test cases within both benchmarks, which were manually checked, confirmed and fixed, resulting in better and more reliable benchmarks. The evaluation also shows that SpeCS is highly efficient and that compared to the state-of-the-art solvers, it gives more precise results in a shorter amount of time. In addition, SpeCS has the highest coverage of the supported language constructs.

Global utilization of online information for substance use disorder: An infodemiological study of Google and Wikipedia from 2004 to 2022.

The increasing number of people who use drugs (PWUDs) can be attributed to the rising online sales of drugs and other related substances. Information on drugs and drug markets has also become easily accessible in web-search engines and social media. Aside from providing direct care, nurses have essential roles in preventing substance use disorder. These roles include health education, liaison, and researcher. Thus, nurses must examine and utilize the Internet, where information and transactions related to these substances are increasing. This study utilized an infodemiological design in exploring the worldwide information utilization for substance use disorder. Data were gathered from Google Trends and Wikimedia Pageview. The data included relative search volumes (RSV), top and rising related queries and topics, and Wikipedia page views between 2004 and 2022. After describing the data, autoregressive integrated mean averaging (ARIMA) models were used to predict future utilization of online information from Google and Wikipedia. Google trends ranked 37 countries based on the search volumes for substance use disorder. Ethiopia, Finland, the United States, Kenya, and Canada have the highest RSVs, while the lowest-ranked country is Turkey, followed by Mexico, Spain, Japan, and Indonesia. Google searches for substance use disorder-related information increased by more than 900% between 2004 and 2022. In addition, Wikipedia page views for substance use disorder-related information increased by almost 200% between 2015 and 2022. Based on the ARIMA models, RSVs and page views are predicted to increase by about 150% and 120% by December 2025. Top and rising search-related topics and queries revealed that the public increasingly utilized online information to understand specific substances and the possible mental health comorbidities related to substance use disorders. Their recent concerns revolved around diagnostics, specific substances, and specific disorders. The Internet can be of paradoxical use in substance use disorder. It has been previously reported to be increasingly used in drug trades, contributing to the increasing prevalence of substance use disorder. Likewise, the present study's findings revealed that it is increasingly utilized for substance use disorder-related information. Thus, nurses and other healthcare professionals should ensure that online information regarding substance use disorders is accurate and up-to-date. Nurse informaticists can form and lead Internet- and social-media-based health teams that perform national infodemiological investigations to assess online information. In doing so, they can inform, expand, and contextualize ehealth substance use education and strengthen the accessibility and delivery of substance use healthcare. In addition, public health nurses can collaborate to engage patients and communities in identifying harmful substance use disorder information online and creating culturally-appropriate messages that will correct misinformation and improve ehealth literacy, specifically in substance use disorder.

A spatial data model for accessibility with indoor temporal changes

AbstractIndoor accessibility is often evaluated with the networks extracted from buildings. For instance, circulation networks with depth cost are applied to floor design. A group of data models supports the extraction of indoor networks from buildings, and these networks are widely used to evaluate the accessibility of users. However, the existing cases are mostly devised for static environments, that is, without consideration of dynamic obstacles. This assumption may contradict the fact of daily changes such as pedestrian flow and temporal blockage. Except emergency scenarios, these daily temporal changes can interrupt the continuous movement of a user, and they shall be considered for data modeling of indoor networks. Thus, this article proposes a concise data model which can manage daily temporal changes of accessibility and support related accessibility queries. In the data model, we consider indoor accessibility on two levels of detail, that is, the room and sub‐room levels. On the room level, we revise a gravity model to measure the accessibility variation of rooms influenced by temporal obstacles. On the sub‐room level, we continuously update obstacle‐avoiding networks adapted to temporal obstacles. In this case, the accessibility of a user at any location can be assessed and time‐dependent accurate routes can be planned at different moments. We implemented this spatial data model in the DBMS PostgreSQL and its feasibility was demonstrated with the test in a campus building. The data model can be applied to daily checks of user accessibility and can be further incorporated with indoor sensor systems and simulation models to predict accessibility variation.

An Invigorating Perspective of a Practical Aspect of NoSQL Database

The main problem has been managing databases because they are continuously expanding quickly and getting more complicated in terms of volume, variety, and velocity. Currently, Relational Database Management Systems (RDMS), also known as SQL, or traditional search engines are primarily used to manage and utilize data collection. Due to their highly semantic properties and applications, relational databases have emerged as powerful and superior during the previous two decades. The handling of a high volume of data and the variability in data type and structure has become a laborious task since Big Data entered the IT industry. Relational databases are inadequate to handle such big data due to their rigid data limitations, structure, relations, and other factors. As a result, data aggregation is rendered impossible. NoSQL databases offer a practical and understandable foundation for combining massive amounts of data, structures, and interactions. Data modeling and migration are now needed in order to define the issue. There isn't yet a tool for switching from relational to no-SQL databases. For this migration, relational (SQL) database queries must be converted to NoSQL database queries. Since relational databases (RDBMS) have found it difficult to keep up with modernization, NoSQL has emerged as the most practical database. This essay aims to highlight and assess the applications of NoSQL, the notion of NoSQL data modeling, and the NoSQL database migration procedure. NoSQL, NoSQL Roadmap, Data Modeling, ETL Approach for Data Migration, Data Loading, and Data Extraction.

Comparative Evaluation of Techniques for n-way Stream Joins in Wireless Sensor Networks

In wireless sensor networks, sensor data are accessed using relational queries. Join queries are commonly used to retrieve the data from multiple tables stored in different parts of a wireless sensor network. However, such queries require large amounts of energy. Many studies have intended to reduce query energy consumption. However, most of the proposed techniques addressed binary joins which are performed between static tables. N-way joins between data streams were rarely considered. Join queries using data streams work continuously and require increasing energy, which is why n-way joins involving several tables consume so much energy. Thus, the challenge lies in reducing energy dissipation. Additionally, it is necessary to determine the appropriate execution order for an n-way join. The number of possible implementations of an n-way join grows exponentially with the tables’ number. In this paper, interesting approaches for n-way joins between streams of data are evaluated. The methods that have been compared are extern-join, Sens-join of Stern et al, and the two techniques NSLJ (N-way Stream Local Join) and NSLSJ (N-way Stream Local Semi-Join). Comparisons are conducted according to several parameters to determine which use case is appropriate for each technique. NSLSJ works best for join queries with low join selectivity factors, while extern-join is more suitable for queries with very high selectivity factors.

A Deeper Examination of NoSQL Database Models and Characteristics

The main problem has been managing databases because they are continuously expanding quickly and getting more complicated in terms of volume, variety, and velocity. Currently, Relational Database Management Systems (RDMS), also known as SQL, or traditional search engines are primarily used to manage and utilize data collection. Due to their highly semantic properties and applications, relational databases have emerged as powerful and superior during the previous two decades. The handling of a high volume of data and the variability in data type and structure has become a laborious task since Big Data entered the IT industry. Relational databases are inadequate to handle such big data due to their rigid data limitations, structure, relations, and other factors. As a result, data aggregation is rendered impossible. NoSQL databases offer a practical and understandable foundation for combining massive amounts of data, structures, and interactions. Data modeling and migration are now needed in order to define the issue. There isn't yet a tool for switching from relational to no-SQL databases. For this migration, relational (SQL) database queries must be converted to NoSQL database queries. Since relational databases (RDBMS) have found it difficult to keep up with modernization, NoSQL has emerged as the most practical database. This essay aims to highlight and assess the applications of NoSQL, the notion of NoSQL data modeling, and the NoSQL database migration procedure. NoSQL, NoSQL Roadmap, Data Modeling, ETL Approach for Data Migration, Data Loading, and Data Extraction.

Spatial ecology, optimal control and game theoretical fishing problems.

Of paramount importance in both ecological systems and economic policies are the problems of harvesting of natural resources. A paradigmatic situation where this question is raised is that of fishing strategies. Indeed, overfishing is a well-known problem in the management of live-stocks, as being too greedy may lead to an overall dramatic depletion of the population we are harvesting. A closely related topic is that of Nash equilibria in the context of fishing policies. Namely, two players being in competition for the same pool of resources, is it possible for them to find an equilibrium situation? The goal of this paper is to provide a detailed analysis of these two queries (i.e optimal fishing strategies for single-player models and study of Nash equilibria for multiple players games) by using a basic yet instructive mathematical model, the logistic-diffusive equation. In this framework, the underlying model simply reads [Formula: see text] where K accounts for natural resources, [Formula: see text] for the density of the population that is being harvested and [Formula: see text] encodes either the single player fishing strategy or, when dealing with Nash equilibria, a combination of the fishing strategies of both players. This article consists of two main parts. The first one gives a very fine characterisation of the optimisers for the single-player game where one aims at solving [Formula: see text], under [Formula: see text] and [Formula: see text] constraints on the fishing strategies [Formula: see text]. In particular, we show that, depending on the value of these constraints, this optimal control problem may behave like a convex or, conversely, concave problem. We also provide a detailed analysis of the large diffusivity limit of this problem. In the case where two players are involved, we rather write [Formula: see text] as [Formula: see text] where [Formula: see text], the fishing strategy of the i-th player, also satisfies [Formula: see text] and [Formula: see text] constraints. Defining [Formula: see text] we aim at finding a Nash equilibrium. We prove the existence of Nash equilibria in several different regimes and investigate several related qualitative queries, for instance providing examples of the well-known tragedy of commons. Our study is completed by a variety of numerical simulations that illustrate our results and allow us to formulate open questions and conjectures.

An Invigorating Perspective of a Practical Aspect of NoSQL Database

The main problem has been managing databases because they are continuously expanding quickly and getting more complicated in terms of volume, variety, and velocity. Currently, Relational Database Management Systems (RDMS), also known as SQL, or traditional search engines are primarily used to manage and utilize data collection. Due to their highly semantic properties and applications, relational databases have emerged as powerful and superior during the previous two decades. The handling of a high volume of data and the variability in data type and structure has become a laborious task since Big Data entered the IT industry. Relational databases are inadequate to handle such big data due to their rigid data limitations, structure, relations, and other factors. As a result, data aggregation is rendered impossible. NoSQL databases offer a practical and understandable foundation for combining massive amounts of data, structures, and interactions. Data modeling and migration are now needed in order to define the issue. There isn't yet a tool for switching from relational to no-SQL databases. For this migration, relational (SQL) database queries must be converted to NoSQL database queries. Since relational databases (RDBMS) have found it difficult to keep up with modernization, NoSQL has emerged as the most practical database. This essay aims to highlight and assess the applications of NoSQL, the notion of NoSQL data modeling, and the NoSQL database migration procedure. NoSQL, NoSQL Roadmap, Data Modeling, ETL Approach for Data Migration, Data Loading, and Data Extraction.

Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications

Knowledge graph has become a cutting-edge technology for linking and integrating heterogeneous, cross-domain datasets to address critical scientific questions. As big data has become prevalent in today's scientific analysis, semantic data repositories that can store and manage large knowledge graph data have become critical in successfully deploying spatially explicit knowledge graph applications. This paper provides a comprehensive evaluation of the popular semantic data repositories and their computational performance in managing and providing semantic support for spatial queries. There are three types of semantic data repositories: (1) triple store solutions (RDF4j, Fuseki, GraphDB, Virtuoso), (2) property graph databases (Neo4j), and (3) an Ontology-Based Data Access (OBDA) approach (Ontop). Experiments were conducted to compare each repository's efficiency (e.g., query response time) in handling geometric, topological, and spatial-semantic related queries. The results show that Virtuoso achieves the overall best performance in both non-spatial and spatial-semantic queries. The OBDA solution, Ontop, has the second-best query performance in spatial and complex queries and the best storage efficiency, requiring the least data-to-RDF conversion efforts. Other triple store solutions suffer from various issues that cause performance bottlenecks in handling spatial queries, such as inefficient memory management and lack of proper query optimization.

Design and Implementation of an Interactive English Translation System Based on the Information-Assisted Processing Function of the Internet of Things

As a new type of language expression, interactive translation is widely used in English, and based on the Internet of Things technology, text harmony and glyphs are combined for information exchange. This design adopts B/S mode to realize the relational query of document content. This paper adopts the B/S architecture mode to design, implement, and test its functional modules and related data analysis to provide a friendly interactive English translation system service to the user group. In this paper, the development of the Internet of Things information-assisted translation system mainly focuses on the semantic embedding and correlation analysis based on the Internet of Things as well as the realization of interactive functions. On this basis, an interactive English translation process based on corpus and text data sets is designed and completed. This method can not only effectively solve the problems of large amount of text and high difficulty of expression but also can make the translation more concise and clear to achieve the required expression effect of the original text. At the same time, the system can automatically perform statistical analysis on the translation results to verify the accuracy and completeness of the translation results, providing reference value for subsequent function implementation. In this paper, we mainly study the realization of interactive functions and propose solutions to its existing problems and deficiencies. The experimental results show that the average BLEU of the system in this paper is 0.985, which indicates that the translation quality of the system is good and the interaction with users is improved.

Evaluation of Characteristics and Building Applications of Multi-Recycled Concrete Aggregates from Precast Concrete Rejects.

The construction industry must meet current environmental requirements, mostly those pertaining to the reduction in construction and demolition waste and the consumption of raw materials. The use of recycled concrete aggregates can be part of the solution, but one question that arises is how many times recyclables can be recycled. This unknown involves other related queries regarding the properties and possible uses of repeated recycled concrete aggregates. This research is derived from the precast concrete industry, where multi-recycling is a pressing need. From good-quality parent concretes, three cycles of recycled concrete aggregates were produced and analysed. The final results are promising due to the good quality of the recycled and multi-recycled concrete aggregates obtained. Not only can they be used in low-level applications (backfilling) as usual, but they can also be used for more demanding purposes, such as graded aggregates, cement-treated road bases and concrete pavements. Their use in structural concrete is feasible, but it will be dependent on the water absorption level and the amount of recycled aggregate substitution. This research proves the viability of multi-recycled concrete aggregates with all of the associated environmental benefits.

Strongly-Normalizing Higher-Order Relational Queries

Language-integrated query is a powerful programming construct allowing database queries and ordinary program code to interoperate seamlessly and safely. Language-integrated query techniques rely on classical results about the nested relational calculus, stating that its queries can be algorithmically translated to SQL, as long as their result type is a flat relation. Cooper and others advocated higher-order nested relational calculi as a basis for language-integrated queries in functional languages such as Links and F#. However, the translation of higher-order relational queries to SQL relies on a rewrite system for which no strong normalization proof has been published: a previous proof attempt does not deal correctly with rewrite rules that duplicate subterms. This paper fills the gap in the literature, explaining the difficulty with a previous proof attempt, and showing how to extend the $\top\top$-lifting approach of Lindley and Stark to accommodate duplicating rewrites. We also show how to extend the proof to a recently-introduced calculus for heterogeneous queries mixing set and multiset semantics.