Optimizing SPARQL queries using shape statistics

With the growing popularity of storing data in native RDF, we witness more and more diverse use cases with complex SPARQL queries. As a consequence, query optimization - and in particular cardinality estimation and join ordering - becomes even more crucial. Classical methods exploit global statistics covering the entire RDF graph as a whole, which naturally fails to correctly capture correlations that are very common in RDF datasets, which then leads to erroneous cardinality estimations and suboptimal query execution plans. The alternative of trying to capture correlations in a fine-granular manner, on the other hand, results in very costly preprocessing steps to create these statistics. Hence, in this paper we propose shapes statistics, which extend the recent SHACL standard with statistic information to capture the correlation between classes and properties. Our extensive experiments on synthetic and real data show that shapes statistics can be generated and managed with only little overhead without disadvantages in query runtime while leading to noticeable improvements in cardinality estimation

ODIN: A dataspace management system

ODIN is a system that supports the incremental pay-as-you-go integration of data sources into dataspaces and provides user-friendly querying mechanisms on top of them. We describe its main characteristics and underlying assumptions, including the user interactions required. Odin’s novelty lies in a largely automated bottom-up approach (i.e., driven by the sources at hand) that includes the user in the loop for disambiguation purposes. The on-site demonstration will feature an ongoing project with the World Health Organization (WHO). Online demo and videos: www.essi.upc.edu/dtim/odin/Peer ReviewedPostprint (published version

Inspiring health worker motivation with supportive supervision: a survey of lady health supervisor motivating factors in rural Pakistan

BACKGROUND: Community health worker motivation is an important consideration for improving performance and addressing maternal, newborn, and child health in low and middle-income countries. Therefore, identifying health system interventions that address motivating factors in resource-strained settings is essential. This study is part of a larger implementation research project called Nigraan, which is intervening on supportive supervision in the Lady Health Worker Programme to improve community case management of pneumonia and diarrhea in rural Pakistan. This study explored the motivation of Lady Health Supervisors, a cadre of community health workers, with particular attention to their views on supportive supervision. METHODS: Twenty-nine lady health supervisors enrolled in Nigraan completed open-ended structured surveys with questions exploring factors that affect their motivation. Thematic analysis was conducted using a conceptual framework categorizing motivating factors at individual, community, and health system levels. RESULTS: Supportive supervision, recognition, training, logistics, and salaries are community and health system motivatingfactors for lady health supervisors. Lady health supervisors are motivated by both their role in providing supportive supervision to lady health workers and by the supervisory support received from their coordinators and managers. Family support, autonomy, and altruism are individual level motivating factors. CONCLUSIONS: Health system factors, including supportive supervision, are crucial to improving lady health supervisormotivation. As health worker motivation influences their performance, evaluating the impact of health system interventions on community health worker motivation is important to improving the effectiveness of community health worker programs

A Smart City Economy Supported by Service Level Agreements: A Conceptual Study into the Waste Management Domain

The full potential of smart cities is not yet realized, and opportunities continue to exist in relation to the business models which govern service provision in cities. In saying this, we make reference to the waste services made available by councils across cities in the United Kingdom (UK). In the UK, smart waste management (SWM) continues to exist as a service trialed across designated cities, and schemes are not yet universally deployed. This therefore exists as a business model which might be improved so that wider roll-out and uptake may be encouraged. In this paper, we present a proposal of how to revise SWM services through integrating the Internet service provider (ISP) into the relationship alongside home and business customers and the city council. The goal of this model is to give customers the opportunity for a more dynamic and flexible service. Furthermore, it will introduce benefits for all parties, in the sense of more satisfied home and business owners, ISPs with a larger customer base and greater profits, and city councils with optimized expenses. We propose that this is achieved using personalized and flexible SLAs. A proof-of-concept model is presented in this paper, through which we demonstrate that the cost to customers can be optimized when they interact with the SWM scheme in the recommended ways

ARDI: automatic generation of RDFS models from heterogeneous data sources

The current wealth of information, typically known as Big Data, generates a large amount of available data for organisations. Data Integration provides foundations to query disparate data sources as if they were integrated into a single source. However, current data integration tools are far from being useful for most organisations due to the heterogeneous nature of data sources, which represents a challenge for current frameworks. To enable data integration of highly heterogeneous and disparate data sources, this paper proposes a method to extract the schema from semi-structured (such as JSON and XML) and structured (such as relational) data sources, and generate an equivalent RDFS representation. The output of our method complements current frameworks and reduces the manual workload required to represent the input data sources in terms of the integration canonical data model. Our approach consists of production rules at the meta-model level that guarantee the correctness of the model translations. Finally, a tool for implementing our approach has been developed.Peer ReviewedPostprint (author's final draft

Health workers’ perspectives, knowledge and skills regarding community case management of childhood diarrhoea and pneumonia: a qualitative inquiry for an implementation research project “Nigraan” in District Badin, Sindh, Pakistan

BACKGROUND: Pakistan\u27s Lady Health Worker Programme aims to provide care to children sick with pneumonia and diarrhoea, which continues to cause 27 % under-five mortality in Pakistan. The quality of supervision received by Lady Health Workers (LHWs) in the programme influence their knowledge and skills, in turn impacting their ability to provide care. METHODS: This study is part of an implementation research project titled Nigraan (an Urdu word meaning supervisor), and explores LHW and Lady Health Supervisor (LHS) perspectives regarding the role of supervision in improving LHWs performance and motivation in district Badin, Sindh, Pakistan. Their knowledge and skills regarding integrated community casemanagement (iCCM) of diarrhoea and pneumonia were also assessed. Fourteen focus group discussions and 20 in-depth interviews were conducted as part of this qualitative inquiry. Analysis was done using QSR NVivo version 10. RESULTS: Most LHWs and LHSs identified pneumonia and diarrhoea as two major causes of death among children under-five. Poverty, illiteracy, poor hygiene and lack of clean drinking water were mentioned as underlying causes of high mortality due to diarrhoea and pneumonia. LHWs and LHSs gaps in knowledge included classification of dehydration, correctly preparing ORS and prescribing correct antibiotics in pneumonia. Lack of training, delayed salaries and insufficient medicines and other supplies were identified as major factors impeding appropriate knowledge and skill development for iCCM of childhood diarrhoea and pneumonia. LHWs considered adequate supervision and the presence of LHSs during household visits as a factor facilitating their performance. LHWs did not have a preference for written or verbal feedback, but LHSs considered written individual feedback to LHWs to be more useful than group and verbal feedback. CONCLUSION: LHWs have knowledge and skill gaps that prevent them from providing effective care for diarrhoea and pneumonia. Enhanced supportive feedback from LHSs could improve LHWs skills and performance

Exploring health care seeking knowledge, perceptions and practices for childhood diarrhea and pneumonia and their context in a rural Pakistani community

BACKGROUND: Where access to facilities for childhood diarrhea and pneumonia is inadequate, community case management (CCM) is an effective way of improving access to care. In Pakistan, utilization of CCM for these diseases through the Lady Health Worker Program remains low. Challenges of access to facilities persist leading to delayed care and poor outcomes. Estimating caregiver knowledge, understanding their perceptions and practices, and recognizing how these are related to care seeking decisions about childhood diarrhea and pneumonia is crucial to bring about coherence between supply and demand-side practices. METHODS: Data was collected from family caregivers to explore their knowledge, perceptions and practices regarding childhood diarrhea and pneumonia. Data from a household survey with 7025 caregivers, seven focus group discussion (FGDs), seven in-depth interviews (IDIs), and 20 detailed narrative interviews are used to explore caregiver knowledge, perceptions and practices. RESULTS: Household survey shows that most family caregivers recognize main signs and symptoms of diarrhea such as loose stools (76%). Fewer recognize signs and symptoms of pneumonia such as breathing problems (21%). Few caregivers (18%) have confidence in lady health workers\u27 (LHWs) ability to treat childhood diarrhea and pneumonia. Care seeking from LHWs remains negligible (\u3c 1%). Caregivers overwhelmingly prefer to seek care from doctors (97%). Seventy-five percent caregivers sought care from private providers and 45% from public providers. FGDs, IDIs, and narrative interviews show that care mostly begins with home remedies and sometimes self-prescribed medicines. Treatment delays occur because of caregiver inability to recognize disease, use of home remedies, financial constraints, and low utilization of community based LHW services. Caregivers do not seek care from LHWs because of lack of trust and LHWs\u27 inability to provide medicines. If finances allow, private doctors, who caregivers perceive as more responsive, are preferred over public sector doctors. Financial resources, availability of time, support for household chores by family and community determine whether, when, and from whom caregivers seek care. CONCLUSIONS: Many children do not receive recommended diarrhea and pneumonia treatment on time. Taking into consideration caregiver concerns, adequate supply of medicines to LHWs, improved facility level care could improve care seeking practices and child health outcomes

Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management

The increase of mental illness cases around the world can be described as an urgent and serious global health threat. Around 500 million people suffer from mental disorders, among which depression, schizophrenia, and dementia are the most prevalent. Revolutionary technological paradigms such as the Internet of Things (IoT) provide us with new capabilities to detect, assess, and care for patients early. This paper comprehensively survey works done at the intersection between IoT and mental health disorders. We evaluate multiple computational platforms, methods and devices, as well as study results and potential open issues for the effective use of IoT systems in mental health. We particularly elaborate on relevant open challenges in the use of existing IoT solutions for mental health care, which can be relevant given the potential impairments in some mental health patients such as data acquisition issues, lack of self-organization of devices and service level agreement, and security, privacy and consent issues, among others. We aim at opening the conversation for future research in this rather emerging area by outlining possible new paths based on the results and conclusions of this work.Consejo Nacional de Ciencia y Tecnologia (CONACyT)Sonora Institute of Technology (ITSON) via the PROFAPI program PROFAPI_2020_0055Spanish Ministry of Science, Innovation and Universities (MICINN) project "Advanced Computing Architectures and Machine Learning-Based Solutions for Complex Problems in Bioinformatics, Biotechnology and Biomedicine" RTI2018-101674-B-I0

Incremental schema integration for data wrangling via knowledge graphs

Virtual data integration is the current approach to go for data wrangling in data-driven decision-making. In this paper, we focus on automating schema integration, which extracts a homogenised representation of the data source schemata and integrates them into a global schema to enable virtual data integration. Schema integration requires a set of well-known constructs: the data source schemata and wrappers, a global integrated schema and the mappings between them. Based on them, virtual data integration systems enable fast and on-demand data exploration via query rewriting. Unfortunately, the generation of such constructs is currently performed in a largely manual manner, hindering its feasibility in real scenarios. This becomes aggravated when dealing with heterogeneous and evolving data sources. To overcome these issues, we propose a fully-fledged semi-automatic and incremental approach grounded on knowledge graphs to generate the required schema integration constructs in four main steps: bootstrapping, schema matching, schema integration, and generation of system-specific constructs. We also present NextiaDI, a tool implementing our approach. Finally, a comprehensive evaluation is presented to scrutinize our approach.This work was partly supported by the DOGO4ML project, funded by the Spanish Ministerio de Ciencia e Innovación under project PID2020-117191RB-I00, and D3M project, funded by the Spanish Agencia Estatal de Investigación (AEI) under project PDC2021-121195-I00. Javier Flores is supported by contract 2020-DI-027 of the Industrial Doctorate Program of the Government of Catalonia and Consejo Nacional de Ciencia y Tecnología (CONACYT, Mexico). Sergi Nadal is partly supported by the Spanish Ministerio de Ciencia e Innovación, as well as the European Union – NextGenerationEU, under project FJC2020-045809-I.Peer ReviewedPostprint (published version