bluuebunny/crossref_metadata_2025 huggingface dataset:
Respiratory and metabolic acidosis correction with the ADVanced Organ Support system
Authors: Perez Ruiz de Garibay Aritz , Kellum John A. , Honigschnabel Johannes , Kreymann Bernhard
Published: September 2019

Background

The lung, the kidney, and the liver are major regulators of acid-base balance. Acidosis due to the dysfunction of one or more organs can increase mortality, especially in critically ill patients. Supporting compensation by increasing ventilation or infusing bicarbonate is often ineffective. Therefore, direct removal of acid may represent a novel therapeutic approach. This can be achieved with the ADVanced Organ Support (ADVOS) system, an enhanced renal support therapy based on albumin dialysis. Here, we demonstrate proof of concept for this technology.

Methods

An ex vivo model of either hypercapnic (i.e., continuous CO2supply) or lactic acidosis (i.e., lactic acid infusion) using porcine blood was subjected to hemodialysis with ADVOS. A variety of operational parameters including blood and dialysate flows, different dialysate pH settings, and acid and base concentrate compositions were tested. Comparisons with standard continuous veno-venous hemofiltration (CVVH) using high bicarbonate substitution fluid and continuous veno-venous hemodialysis (CVVHD) were also performed.

Results

Sixty-one milliliters per minute (2.7 mmol/min) of CO2was removed using a blood flow of 400 ml/min and a dialysate pH of 10 without altering blood pCO2and HCO3−(36 mmHg and 20 mmol/l, respectively). Up to 142 ml/min (6.3 mmol/min) of CO2was eliminated if elevated pCO2(117 mmHg) and HCO3−(63 mmol/l) were allowed. During continuous lactic acid infusion, an acid load of up to 3 mmol/min was compensated. When acidosis was triggered, ADVOS multi normalized pH and bicarbonate levels within 1 h, while neither CVVH nor CVVHD could. The major determinants to correct blood pH were blood flow, dialysate composition, and initial acid-base status.

Conclusions

In conclusion, ADVOS was able to remove more than 50% of the amount of CO2typically produced by an adult human. Blood pH was maintained stable within the physiological range through compensation of a metabolic acid load by albumin dialysate. These in vitro results will require confirmation in patients.
Motivational factors for choosing treatment destinations among the patients treated overseas from the United Arab Emirates: results from the knowledge, attitudes and perceptions survey 2012
Authors: Alnakhi Wafa K. , Segal Jodi B. , Frick Kevin D. , Ahmed Saifuddin , Morlock Laura
Published: September 2019

Background

Travelling seeking healthcare is becoming common phenomenon. There is limited research to understand factors associated with destinations of choice. Each year the Dubai Health Authority (DHA) spends millions of dollars to cover Emiratis seeking healthcare overseas. The objective of this study is to examine the association of treatment destinations, patients’ characteristics and motivation factors among the patients treated overseas from the UAE during 2009–2012.

Method

The data from the Knowledge, Attitudes and Perceptions Survey 2012 in Dubai on medical travel. Examining destinations by patients’ characteristics and motivational factors under push and pull factor framework. Modified Poisson regression model was used to identify factors associated with treatment destinations.

Results

Three hundred thirty-six UAE national families with a member who sought overseas treatment during 2009–2012 were analyzed for this study regarding their most recent trip. The aim of the survey is to explore their knowledge, attitudes and perceptions. The majority of respondents were family members not the patients who had experienced the medical treatment overseas (63%). Germany was the top treatment destination (45%). The top 3 medical conditions for which people traveled overseas were cancer (17%), bone and joint diseases (16%), and heart diseases (15%). However, patients diagnosed with stroke (brain hemorrhage or clot) are more likely to travel to Germany for medical treatment while patients diagnosed with eye diseases are more likely to seek medical treatment at other destinations. Cost was a primary motivational factor for choosing a treatment destination.

Conclusion

This study addressed knowledge gap related medical travel in the UAE. The results provided evidence about perceptions when choosing treatment destinations. Medical condition and financial factors were main predictors for choosing treatment destination. The result will influence policies related financial coverage by the government. The results suggest understanding patients’ perceptions in-depth related their medical conditions and financial factors for better regulation of overseas treatment strategy in the UAE.
Effect of Garcinia kola seeds supplemented diet on growth performance and gonadal development of Oreochromis niloticus juveniles breed in ponds
Authors: Nyadjeu Paulin , Angoun Jeannette , Ndasi Ngwasiri Pride , Tabi-Tomedi Minette Eyango
Published: September 2019

Background

Despite the favorable geo-climatic potential of Cameroon, the national production of tilapia remains low due to poor tilapia growth reported by fish farmers. One of the underlying reasons is the early female maturation at a very small size and precocious breeding in earthen ponds, resulting in overpopulation which leads to stunted growth and therefore to the production of unmarketable fish size. Studies have shown that dietary supplementation of G. kola enhanced growth in young Clarias gariepinus and Oreochromis niloticus. It was also reported that G. kola inhibited spawning in Tilapia adult females. Therefore, this study sought to assess the effects of Garcinia kola as growth promoter and inhibitor of gonadal development in young Oreochromis niloticus.

Methods

A total of 108 juveniles weighing 13.32 ± 0.62 g were randomly distributed in 9 hapas of 12 fishes each (9 females and 3 males) and fed for 70 days with three isonitrogenous diets, 40% crude protein with increasing Garcinia kola supplementation levels of 0 (normal diet), 6% and 10% (experimental diets). Physico-chemical parameters of the water (temperature, dissolved oxygen, pH, nitrate, nitrite, ammonia, and transparency) were measured twice a week. Every 14 days, fish were harvested, counted, and weighed. At the end of the experiment, three fish of each sex per replicate were sacrificed and their gonad and liver collected and weighed. Data were statistically analyzed using one-way analysis of variance repeated measure followed by Newman-Keuls multiple tests.

Results

The results showed that all physico-chemical parameters of the water were within the recommended values for Tilapia culture. Tilapia fed 6% Garcinia kola supplemented diet displayed higher final body weight in males (38.60 ± 3.50 g) and females (36.77 ± 3.62 g) compared to those receiving normal diet (36.23 ± 1.36 g and 25.87 ± 3.32 g; respectively to the final body weight in males and females). The gonadosomatic index and hepatosomatic index indicated no significant variation in males while in females, these were significantly low in the experimental fish compared to control fish.

Conclusion

The results of this study demonstrated that supplementation of G. kola seeds in diets of young Tilapia improved growth performance and impaired gonadal development in females.
Indonesian food culture mapping: a starter contribution to promote Indonesian culinary tourism
Authors: Wijaya Serli
Published: September 2019

The food culture of Indonesia is shaped by several factors such as nature, history, and culture. With its enormous geographic and cultural diversity across the archipelagos, it is evident that Indonesian cuisine is rich in variety and taste. As such, food can be utilised as a strategic means to boost the tourism industry of the country. In the past 5 years, the Indonesian government has given a great support for the development of culinary tourism as one special interest tourism sector that is promoted extensively to the international market. Promoting Indonesian culinary tourism should not be merely exposing the ample varieties of the traditional food that Indonesia has, but more importantly, telling the market about the socio-cultural values behind the food itself. This study aimed to portray how Indonesian food culture has been shaped, developed, and held as the value embedded in the society and has been passed from one generation to the next. For the purpose of the study, a range of literature from journal articles, books, archives, magazines, and articles to Internet sources that are relevant to Indonesian culinary discussions was reviewed.
Additive value of 3D-echo in prediction of immediate outcome after percutaneous balloon mitral valvuloplasty
Authors: Farrag Hazem M. A. , Setouhi Amr M. , El-Mokadem Mustafa O. , El-Swasany Mustafa A. , Mahmoud Khalid S. , Mahmoud Hesham B. , Ibrahim Alaa M.
Published: September 2019

Background

Results of percutaneous balloon mitral valvuloplasty (BMV) are basically dependent on suitable patient selection. Currently used two-dimensional (2D) echocardiography (2DE) scores have many limitations. Three-dimensional (3D) echocardiography (3DE)-based scores were developed for better patient selection and outcome prediction. We aimed to compare between 3D-Anwar and 2D-Wilkins scores in mitral assessment for BMV, and investigate the additive value of 3DE in prediction of immediate post-procedural outcome. Fifty patients with rheumatic mitral stenosis and candidates for BMV were included. Patients were subjected to 2D- and real-time 3D-transthoracic echocardiography (TTE) before and immediately after BMV for assessing MV area (MVA), 2D-Wilkins and 3D-Anwar score, commissural splitting, and mitral regurgitation (MR). Transesophageal echocardiography (TEE) was also undertaken immediately before and intra-procedural. Percutaneous BMV was performed by either multi-track or Inoue balloon technique.

Results

The 2DE underestimated post-procedural MVA than 3DE (p = 0.008). Patients with post-procedural suboptimal MVA or significant MR had higher 3D-Anwar score compared to 2D-Wilkins score (p = 0.008 and p = 0.03 respectively). The 3D-Anwar score showed a negative correlation with post-procedural MVA (r = − 0.48, p = 0.001). Receiver operating characteristic (ROC) curve analysis for both scores revealed superior prediction of suboptimal results by 3D-Anwar score (p < 0.0001). The 3DE showed better post-procedural posterior-commissural splitting than 2DE (p = 0.004). Results of both multi-track and Inoue balloon were comparable except for favorable posterior-commissural splitting by multi-track balloon (p = 0.04).

Conclusion

The 3DE gave valuable additive data before BMV that may predict immediate post-procedural outcome and suboptimal results.
BIRADS 4 breast lesions: comparison of contrast-enhanced spectral mammography and contrast-enhanced MRI
Authors: Yasin Rabab , El Ghany Enas Abd
Published: September 2019

Background

Breast cancer is the most common cancer in women worldwide. It is responsible for about 23% of cancer in females in both developed and developing countries [1]. We aimed to assess the accuracy of contrast-enhanced spectral mammography (CESM) versus contrast-enhanced breast MRI in the evaluation of BIRADS 4 breast lesions.

Results

Fifty patients were included in this study; there were 28 malignant cases and 22 benign cases; all cases were proved by histopathological result either by core biopsy or excision biopsy. CESM was found to have less sensitivity (94.1%) than MRI (100%) but CESM has higher specificity (100%) than MRI (95.5%). The accuracy of CESM was 96.4%, while the accuracy of MRI was 98.2% with no statistical significance (Pvalue 0.827).

Conclusion

CESM can be used as a sensitive diagnostic tool in the detection and staging of breast cancer with higher specificity and less sensitivity as compared to contrast enhanced breast MRI.
Role of lumbar spine signal intensity measurement by MRI in the diagnosis of osteoporosis in post-menopausal women
Authors: Saad Maha M. , Ahmed Ahmed Tohamy , Mohamed Khaled E. , Habba Mohamed R.
Published: September 2019

Background

Osteoporosis is a worldwide health problem and a common cause of bone fractures; the most common type of osteoporosis is post-menopausal type. MRI has a role in the diagnosis of osteoporosis and can be used as a screening tool, so the purpose of our study was to define a quantitative MRI-based score (M-score) for the detection of lumbar spine osteoporosis and to evaluate the correlation between lumbar spine signal intensity measured by MRI and BMD (bone mineral density) in post-menopausal women. Our case-control study involved 100 cases (50 old post-menopausal females as a case group and 50 healthy females as a control group of matched age). Both groups were subjected to history taking, dual-energy X-ray absorptiometry (DEXA), and conventional lumbar MRI. DEXA was performed for the lumbar spine and all scores (T-score, Z-score, BMD) were calculated. Lumbar MRI was performed (sagittal T1WI and T2WI) from L1–L4 levels. SNRL1–L4 and M-score were calculated from T1W images.

Results

All DEXA scores were significantly lower in post-menopausal females compared to the control group (P < 0.0001). Meanwhile, SNRL1–L4 and M-score were significantly higher among cases than controls (P < 0.0001). The diagnostic threshold of SNRL1–L4 and M-score for distinguishing osteoporotic from non-osteoporotic females was 104.5 for SNRL1–L4 with a sensitivity of 94%, specificity 60%, positive predictive values (PPV) 31%, and negative predictive values (NPV) 98%, and 3.5 for M-score with a sensitivity of 93.3%, specificity 83.5%, PPV 50%, and NPV 98.6%. SNRL1–L4 was negatively correlated with M-score (r = − 0.74, P < 0.0001), T-score (r = − 0.42, P < 0.0001), Z-score (r = − 0.32, P < 0.0001), and BMD (r = − 0.31, P < 0.0001). M-score was negatively correlated with T-score (r = − 0.48, P < 0.0001), Z-score (r = − 0.36, P < 0.0001), and BMD (r = − 0.37, P < 0.0001). M-score moderately agreed with T-score (κ = 0.5, P < 0.0001).

Conclusion

The MRI-based score (M-score) of the lumbar spine is an accurate quantitative method for distinguishing osteoporotic from non-osteoporotic females. M-score and SNRL1–L4 are significantly correlated with osteoporotic indices (T-score and BMD).
Increased Infection Risk in Addison’s Disease and Congenital Adrenal Hyperplasia
Authors: Tresoldi Alberto S , Sumilo Dana , Perrins Mary , Toulis Konstantinos A , Prete Alessandro , Reddy Narendra , Wass John A H , Arlt Wiebke , Nirantharakumar Krishnarajah
Published: September 2019

Context

Mortality and infection-related hospital admissions are increased in patients with primary adrenal insufficiency (PAI). However, the risk of primary care–managed infections in patients with PAI is unknown.

Objective

To estimate infection risk in PAI due to Addison’s disease (AD) and congenital adrenal hyperplasia (CAH) in a primary care setting.

Design

Retrospective cohort study using UK data collected from 1995 to 2018.

Main outcome measures

Incidence of lower respiratory tract infections (LRTIs), urinary tract infections (UTIs), gastrointestinal infections (GIIs), and prescription counts of antimicrobials in adult PAI patients compared to unexposed controls.

Results

A diagnosis of PAI was established in 1580 AD patients (mean age 51.7 years) and 602 CAH patients (mean age 35.4 years). All AD patients and 42% of CAH patients were prescribed glucocorticoids, most frequently hydrocortisone in AD (82%) and prednisolone in CAH (50%). AD and CAH patients exposed to glucocorticoids, but not CAH patients without glucocorticoid treatment, had a significantly increased risk of LRTIs (adjusted incidence rate ratio AD 2.11 [95% confidence interval (CI) 1.64–2.69], CAH 3.23 [95% CI 1.21–8.61]), UTIs (AD 1.51 [95% CI 1.29–1.77], CAH 2.20 [95% CI 1.43–3.34]), and GIIs (AD 3.80 [95% CI 2.99–4.84], CAH 1.93 [95% CI 1.06–3.52]). This was mirrored by increased prescription of antibiotics (AD 1.73 [95% CI 1.69–1.77], CAH 1.77 [95% CI 1.66–1.89]) and antifungals (AD 1.89 [95% CI 1.74–2.05], CAH 1.91 [95% CI 1.50–2.43]).

Conclusions

There is an increased risk of infections and antimicrobial use in PAI in the primary care setting at least partially linked to glucocorticoid treatment. Future studies will need to address whether more physiological glucocorticoid replacement modes could reduce this risk.
“Weak Thought” and the Reduction of Violence
Authors: Vattimo Gianni , Zabala Santiago , Mascetti Translated by Yaakov
Published: September 2019

In this interview Vattimo discusses with Zabala the possibility of a nihilist philosophy of law as an alternative to the idea of justice and the violence that predictably results from it. To make this substitution would involve the redirection of humanity away from its self-understanding as progressively approaching a metaphysical truth that is eternal and toward the acceptance of an already existing “polytheism of values,” where truth is a contingent and changing product of discursiveness. A society that structures its legal system on what Vattimo terms “optimistic nihilism” would dismiss any urge for the unity and strength supposedly characteristic of monoculturalism and univocal values. Such cohesion as is possible must come from flexibility, responsiveness to contingency, and an openness to multivocal values. “To apply justice to human affairs,” Vattimo argues, is no more than “to adjust things” as conditions change.
A Reply to Paul Feyerabend and Richard Rorty
Authors: Kermode Frank
Published: September 2019

This retitled excerpt from Frank Kermode’s introduction to the symposium “Beyond Post-: A Revaluation of the Revaluation of All Values” (Common Knowledge 1:3 [Winter 1992]: 10–12) is republished here in a special issue of representative pieces from the journal’s first twenty-five years. Kermode had called for papers in the journal’s inaugural issue (1:1 [Spring 1992]: 5–6) on “the question of value” and was to a degree disappointed with the results. He had wanted the ensuing symposium to treat and even focus on axiology in the arts, but the papers answering his call mainly dealt with political and ethical matters. In this introduction he replies chiefly to pieces by the philosophers Richard Rorty and Paul Feyerabend dealing with poverty, famine, sickness, and environmental concerns, but Kermode also addresses and mostly agrees with arguments made in Common Knowledge 1:1 by the philosopher Bernard Williams. (Williams’s article, “Left-Wing Wittgenstein,” is likewise reprinted in the 2019 anniversary issue.) Kermode’s piece concludes by restating the hope of his call for papers that “the matter of literary value” will be treated “in the broad context” that Common Knowledge provides “for discussion of the philosophy of value. Precedence must obviously be given to a planet at risk, but to lose what has been valued in literature is a sure way of increasing the unpleasantness of the future.”
The Catholic Church Vis-à-Vis Liberal Society
Authors: Etchegaray Roger Cardinal , Chang Translated by Mei Lin
Published: September 2019

Cardinal Etchegaray argues here that the dialogue between church and state, with both parties rooted in sometimes conflicting absolute claims and values, has become more recently a wider-ranging dialogue between the church and a pluralist, relativist liberal society. The very definition of “liberal society” is open to argument, and the church may find elements to commend or oppose in any given definition. Since the nineteenth century the church has often found itself in opposition to various ideas of “liberty,” especially those that represent an idolatry of absolute rights that push aside Christian spiritual and moral concerns. Now that liberalism has become the pervasive model for society, the church finds it may more easily express its critique, with the aim of making society more conducive to allowing people to become fully human. Indeed, the church provides a necessary check on the excesses of liberal society, particularly those of capitalism and democratic populism. Its essential point is the transcendent dimension of the human person—our connection with the divine. The pursuit of economic and political ends needs to be governed by a concern for the ethical, itself founded on the divine. Liberal society will only live up to its own highest aspirations through promoting self-mastery and an awareness that humanity’s freedom is ultimately found only in God.
IAEA survey of dental cone beam computed tomography practice and related patient exposure in nine Central and Eastern European countries
Authors: Beganović Adnan , Ciraj-Bjelac Olivera , Dyakov Iliya , Gershan Vesna , Kralik Ivana , Milatović Aleksandra , Šalát Dušan , Stepanyan Karapet , Vladimirov Anatoli , Vassileva Jenia
Published: September 2019

Objectives:

Cone beam CT (CBCT) in dentistry and maxillofacial surgery is a widely used imaging method for the assessment of various maxillofacial and dental pathological conditions. The objective of this study was to summarize the results of a multinational retrospective–prospective study that focused on patient exposure in this modality.

Methods:

The study included 27 CBCT units and 325 adult and paediatric patients, in total. Data on patients, clinical indications, technical parameters of exposure, patient dose indicator, or, alternatively, dose to phantom were collected. The dose indicator used was air kerma–area product, PKA.

Results:

In most scanners operators are offered with a variety of options regarding technical parameters, especially the field of view size. The median and the third quartile value of PKAfor adult patients in 14 different facilities were 820 mGy cm² and 1000 mGy cm² (interquartile range = 1058 mGy cm²), and 653 mGy cm² and 740 mGy cm² (interquartile range = 1179 mGy cm²) for children, as reported by four different institutions. Phantom dose data were reported from 15 institutions, and median PKAranged from 125 mGy cm² to 1951 mGy cm². Median PKAvalues varied by more than a 10-fold between institutions, mainly due to differences in imaging protocol used, in particular field of view and tube current-exposure time product.

Conclusions:

The results emphasize the need for a cautious approach to using dental CBCT. Imaging only when the clinical indications are clear, accompanied with the appropriate radiographic techniques and the optimum imaging protocol, will help reduce radiation dose to patients.
Imaging of root canal treatment using ultra high field 9.4T UTE-MRI – a preliminary study
Authors: Timme Maximilian , Masthoff Max , Nagelmann Nina , Masthoff Malte , Faber Cornelius , Bürklein Sebastian
Published: September 2019

Objectives:

To investigate the potential of 9.4T ultrashort echo time (UTE) technology visualizing tooth anatomy and root canal treatment in vitro. In particular, it was evaluated whether the currently achievable resolution is suited presenting all anatomical structures and whether the root canal filling materials are distinguishable in UTE-MRI.

Methods:

Four extracted human teeth were examined using 9.4T UTE-MRI prior endodontic treatment (native teeth), after preparation and after obturation procedure. Root canal obturation was performed using warm vertical compaction (Schilder technique) with an epoxy-resin-based sealer. A single gutta-percha cone measured by MRI served as intensity-reference. MRI results were validated with corresponding histologic sections of the teeth. In addition, all teeth were examined at the different stages with CBCT and conventional X-ray.

Results:

9.4T UTE-MRI enabled a precise visualization of root canal anatomy of all teeth at a resolution of 66 µm. After obturation, dentin, sealer and gutta-percha cones showed distinct MRI signal changes that allowed clear differentiation of the obturation materials from surrounding tooth structure. The filling materials, isthmal root canal connections and even dentin-cracks that were identified in the MR-images could be verified in histological sections.

Conclusions:

9.4T UTE-MRI is suitable for visualization of root canal anatomy, the evaluation of root canal preparation and obturation with a high spatial resolution and may provide a versatile tool for dental material research in endodontics.
Vaccine hesitancy among parents in Kuala Lumpur: a single center study
Authors: Musa Ahmad Farouk , Soni Trived , Cheong Xian Pei , Nordin Rusli Bin
Published: September 2019

Background: Vaccine hesitancy (VH) is defined as the delay in acceptance or refusal of vaccination despite availability of vaccination services. The main objective of this study was to improve the understanding of vaccine hesitancy (VH) among parents in Kuala Lumpur (KL), Malaysia, by determining the prevalence of VH among parents and to identify the predictors associated with a vaccine hesitant attitude.

Methods: This cross-sectional study was conducted in KL. A questionnaire was devised to collect information from parents, namely sociodemographic information, WHO determinants of VH and the Parents Attitude towards Childhood Vaccine (PACV) scale.

Results: A total of 380 questionnaires were distributed and 337 were returned (88.6% response rate). Those that completed 49 (>90%) out of the 55 given questions in the survey were included for data analysis. Based on inclusion and exclusion criteria, 23 were excluded, giving a sample size of 314. To identify parents, those with at least one child were included, giving a sample size of 221. We noted 60.2% (189) of the participants were females and 80.3% (252) were Malay. Our study found a prevalence of VH of 14.5% among parents based on the 15-item PACV scale.  Univariate analysis found no link between sociodemographic factors and VH in parents. Only five of these determinants were included in the final model as statistically significant (p< 0.05) predictors of VH among parents in KL. The five factors were introduction to a new vaccine, negative past experiences of vaccinations, distrust of the pharmaceutical industry, distrust of health systems and providers and being male.

Conclusions: Factors contributing towards a prevalence of VH of 14.5% in KL, Malaysia must be studied further to identify any temporal relationship to the under-immunization of children in order to reach the WHO goal of 100% immunization coverage in children and eradication of vaccine preventable diseases.
Spindle epithelial tumour with thymus-like differentiation: report of two cases
Authors: Karaisli S , Haciyanli M , Gücek Haciyanli S , Tavusbay C , Gur EO , Kamer E , Arikan Etit D
Published: September 2019

Spindle epithelial tumour with thymus-like differentiation (SETTLE) is a very rare neck malignancy arising from thymic tissue or branchial pouch, mostly seen in the early decades of life. SETTLE is thought to be a low-grade neoplasm but distant metastases have been reported. Fewer than 50 cases have been reported in the PubMed database.

We report on the four-year follow-up for a 13-year-old boy, with no signs of recurrence or distant metastasis. In addition, a 45-year-old woman was admitted to hospital with neck mass. Fine-needle aspiration biopsy was compatible with medullary thyroid carcinoma but serum levels of calcitonin were normal. Total thyroidectomy and central compartment lymph node dissection was planned with an initial diagnosis of medullary thyroid carcinoma. At surgery, frozen section analysis converted our diagnosis to SETTLE and the intraoperative strategy was changed.

SETTLE should be considered in a patient with normal serum calcitonin levels in the presence of suspicious medullary thyroid carcinoma at fine-needle aspiration biopsy. Frozen section analysis may help to manage the intraoperative strategy. This report describes the first case of SETTLE successfully diagnosed by frozen section analysis.
NETS
Authors: Yoon Susik , Lee Jae-Gil , Lee Byung Suk
Published: September 2019

This paper addresses the problem of efficiently detecting outliers from a data stream as old data points expire from and new data points enter the window incrementally. The proposed method is based on a newly discovered characteristic of a data stream that the change in the locations of data points in the data space is typically very insignificant. This observation has led to the finding that the existing distance-based outlier detection algorithms perform excessive unnecessary computations that are repetitive and/or canceling out the effects. Thus, in this paper, we propose a novel
set-based
approach to detecting outliers, whereby data points at similar locations are grouped and the detection of outliers or inliers is handled at the group level. Specifically, a new algorithm NETS is proposed to achieve a remarkable performance improvement by realizing set-based early identification of outliers or inliers and taking advantage of the "net effect" between expired and new data points. Additionally, NETS is capable of achieving the same efficiency even for a high-dimensional data stream through
two-level dimensional filtering
. Comprehensive experiments using six real-world data streams show 5 to 25 times faster processing time than state-of-the-art algorithms with comparable memory consumption. We assert that NETS opens a new possibility to real-time data stream outlier detection.
Fast and robust distributed subgraph enumeration
Authors: Ren Xuguang , Wang Junhu , Han Wook-Shin , Yu Jeffrey Xu
Published: September 2019

We study the subgraph enumeration problem under distributed settings. Existing solutions either suffer from severe memory crisis or rely on large indexes, which makes them impractical for very large graphs. Most of them follow a synchronous model where the performance is often bottlenecked by the machine with the worst performance. Motivated by this, in this paper, we propose RADS, a Robust Asynchronous Distributed Subgraph enumeration system. RADS first identifies results that can be found using single-machine algorithms. This strategy not only improves the overall performance but also reduces network communication and memory cost. Moreover, RADS employs a novel
region-grouped multi-round expand verify & filter
framework which does not need to shuffle and exchange the intermediate results, nor does it need to replicate a large part of the data graph in each machine. This feature not only reduces network communication cost and memory usage, but also allows us to adopt simple strategies for memory control and load balancing, making it more robust. Several optimization strategies are also used in RADS to further improve the performance. Our experiments verified the superiority of RADS to state-of-the-art subgraph enumeration approaches.
Ocean vista
Authors: Fan Hua , Golab Wojciech
Published: September 2019

Providing ACID transactions under conflicts across globally distributed data is the Everest of transaction processing protocols. Transaction processing in this scenario is particularly costly due to the high latency of cross-continent network links, which inflates concurrency control and data replication overheads. To mitigate the problem, we introduce Ocean Vista - a novel distributed protocol that guarantees
strict serializability
. We observe that concurrency control and replication address different aspects of resolving the visibility of transactions, and we address both concerns using a multi-version protocol that tracks visibility using version watermarks and arrives at correct visibility decisions using efficient gossip. Gossiping the watermarks enables asynchronous transaction processing and acknowledging transaction visibility in batches in the
concurrency control
and
replication
protocols, which improves efficiency under high cross-datacenter network delays. In particular, Ocean Vista can process
conflicting
transactions in parallel, and supports efficient
write-quorum / read-one
access using one round trip in the common case. We demonstrate experimentally in a multi-data-center cloud environment that our design outperforms a leading distributed transaction processing engine (TAPIR) more than 10-fold in terms of peak throughput, albeit at the cost of additional latency for gossip. The latency penalty is generally bounded by one wide area network (WAN) round trip time (RTT), and in the best case (i.e., under light load) our system nearly breaks even with TAPIR by committing transactions in around one WAN RTT.
Rewriting of plain SO tgds into nested tgds
Authors: Hai Rihan , Quix Christoph
Published: September 2019

Schema mappings express the relationships between sources in data interoperability scenarios and can be expressed in various formalisms. Source-to-target tuple-generating dependencies (s-t tgds) can be easily used for data transformation or query rewriting tasks. Second-order tgds (SO tgds) are more expressive as they can also represent the composition and inversion of s-t tgds. Yet, the expressive power of SO tgds comes with the problem of undecidability for some reasoning tasks. Nested tgds and plain SO tgds are mapping languages that are between s-t tgds and SO tgds in terms of expressivity, and their properties have been studied in the recent years. Nested tgds are less expressive than plain SO tgds, but the logical equivalence problem for nested tgds is decidable. However, a detailed characterization of plain SO tgds that have an equivalent nested tgd is missing. In this paper, we present an algorithmic solution for translating plain SO tgds into nested tgds. The algorithm computes one or more nested tgds, if a given plain SO tgd is rewritable. Furthermore, we are able to give a detailed characterization of those plain SO tgds for which an equivalent nested tgd exists, based on the structural properties of the source predicates and Skolem functions in the plain SO tgd. In the evaluation, we show that our algorithm covers a larger subset of plain SO tgds than previous approaches and that a rewriting can be computed efficiently although the algorithm has the exponential complexity.
Accelerating raw data analysis with the ACCORDA software and hardware architecture
Authors: Fang Yuanwei , Zou Chen , Chien Andrew A.
Published: September 2019

The data science revolution and growing popularity of data lakes make efficient processing of raw data increasingly important. To address this, we propose the ACCelerated Operators for Raw Data Analysis (ACCORDA) architecture. By extending the operator interface (subtype with encoding) and employing a uniform runtime worker model, ACCORDA integrates data transformation acceleration seamlessly, enabling a new class of encoding optimizations and robust high-performance raw data processing. Together, these key features preserve the software system architecture, empowering state-of-art heuristic optimizations to drive flexible data encoding for performance. ACCORDA derives performance from its software architecture, but depends critically on the acceleration of the Unstructured Data Processor (UDP) that is integrated into the memory-hierarchy, and accelerates data transformation tasks by 16x-21x (parsing, decompression) to as much as 160x (deserialization) compared to an x86 core.

We evaluate ACCORDA using TPC-H queries on tabular data formats, exercising raw data properties such as parsing and data conversion. The ACCORDA system achieves 2.9x-13.2x speedups when compared to SparkSQL, reducing raw data processing overhead to a geomean of 1.2x (20%). In doing so, ACCORDA robustly matches or outperforms prior systems that depend on caching loaded data, while computing on raw, unloaded data. This performance benefit is robust across format complexity, query predicates, and selectivity (data statistics). ACCORDA's encoding-extended operator interface unlocks aggressive encoding-oriented optimizations that deliver 80% average performance increase over the 7 affected TPC-H queries.
Comparing synopsis techniques for approximate spatial data analysis
Authors: Siddique A. B. , Eldawy Ahmed , Hristidis Vagelis
Published: September 2019

The increasing amount of spatial data calls for new scalable query processing techniques. One of the techniques that are getting attention is
data synopsis
, which summarizes the data using samples or histograms and computes an approximate answer based on the synopsis. This general technique is used in selectivity estimation, clustering, partitioning, load balancing, and visualization, among others. This paper experimentally studies four spatial data synopsis techniques for three common data analysis problems, namely, selectivity estimation, k-means clustering, and spatial partitioning. We run an extensive experimental evaluation on both real and synthetic datasets of up to 2.7 billion records to study the trade-offs between the synopsis methods and their applicability in big spatial data analysis. For each of the three problems, we compare with baseline techniques that operate on the whole dataset and evaluate the synopsis generation time, the time for computing an approximate answer on the synopsis, and the accuracy of the result. We present our observations about when each synopsis technique performs best.
BlockchainDB
Authors: El-Hindi Muhammad , Binnig Carsten , Arasu Arvind , Kossmann Donald , Ramamurthy Ravi
Published: September 2019

In this paper we present
BlockchainDB
, which leverages blockchains as a storage layer and introduces a database layer on top that extends blockchains by classical data management techniques (e.g., sharding) as well as a standardized query interface to facilitate the adoption of blockchains for data sharing use cases. We show that by introducing the additional database layer, we are able to improve the performance and scalability when using blockchains for data sharing and also massively decrease the complexity for organizations intending to use blockchains for data sharing.
Efficient task-specific data valuation for nearest neighbor algorithms
Authors: Jia Ruoxi , Dao David , Wang Boxin , Hubis Frances Ann , Gurel Nezihe Merve , Li Bo , Zhang Ce , Spanos Costas , Song Dawn
Published: September 2019

Given a data set
D
containing millions of data points and a data consumer who is willing to pay for $
X
to train a machine learning (ML) model over
D
,
how should we distribute this $X to each data point to reflect its "value"?
In this paper, we define the "relative value of data" via the Shapley value, as it uniquely possesses properties with appealing real-world interpretations, such as fairness, rationality and decentralizability. For general, bounded utility functions, the Shapley value is known to be challenging to compute: to get Shapley values for all
N
data points, it requires
O
(2

N

) model evaluations for exact computation and
O
(
N
log
N
) for (
ϵ
, δ)-approximation.

In this paper, we focus on one popular family of ML models relying on
K
-nearest neighbors (
K
NN). The most surprising result is that for unweighted
K
NN classifiers and regressors, the Shapley value of all
N
data points can be computed,
exactly
, in
O
(
N
log
N
) time - an exponential improvement on computational complexity! Moreover, for (
ϵ
, δ)-approximation, we are able to develop an algorithm based on Locality Sensitive Hashing (LSH) with only sublinear complexity
O
(
N

h
(
ϵ
,
K
)

log
N
) when
ϵ
is not too small and
K
is not too large. We empirically evaluate our algorithms on up to 10 million data points and even our
exact
algorithm is up to three orders of magnitude faster than the baseline approximation algorithm. The LSH-based approximation algorithm can accelerate the value calculation process even further.

We then extend our algorithm to other scenarios such as (1) weighed
K
NN classifiers, (2) different data points are clustered by different
data curators
, and (3) there are
data analysts
providing computation who also requires proper valuation.
Some
of these extensions, although also being improved exponentially, are less practical for exact computation (e.g.,
O
(

N
K

) complexity for weigthed
K
NN). We thus propose an Monte Carlo approximation algorithm, which is
O
(
N
(log
N
)
2
/(log
K
)
2
) times more efficient than the baseline approximation algorithm.
Motivo
Authors: Bressan Marco , Leucci Stefano , Panconesi Alessandro
Published: September 2019

The randomized technique of color coding is behind state-of-the-art algorithms for estimating graph motif counts. Those algorithms, however, are not yet capable of scaling well to very large graphs with billions of edges. In this paper we develop novel tools for the "motif counting via color coding" framework. As a result, our new algorithm, MOTIYO, scales to much larger graphs while at the same time providing more accurate motif counts than ever before. This is achieved thanks to two types of improvements. First, we design new succinct data structures for fast color coding operations, and a biased coloring trick that trades accuracy versus resource usage. These optimizations drastically reduce the resource requirements of color coding. Second, we develop an adaptive motif sampling strategy, based on a fractional set cover problem, that breaks the additive approximation barrier of standard sampling. This gives multiplicative approximations for all motifs at once, allowing us to count not only the most frequent motifs but also extremely rare ones.

To give an idea of the improvements, in 40 minutes MOTIVO counts 7-nodes motifs on a graph with 65M nodes and 1.8B edges; this is 30 and 500 times larger than the state of the art, respectively in terms of nodes and edges. On the accuracy side, in one hour MOTIVO produces accurate counts of ≈ 10.000 distinct 8-node motifs on graphs where state-of-the-art algorithms fail even to find the second most frequent motif. Our method requires just a high-end desktop machine. These results show how color coding can bring motif mining to the realm of truly massive graphs using only ordinary hardware.
Optimizing subgraph queries by combining binary and worst-case optimal joins
Authors: Mhedhbi Amine , Salihoglu Semih
Published: September 2019

We study the problem of optimizing subgraph queries using the new worst-case optimal join plans. Worst-case optimal plans evaluate queries by matching one query vertex at a time using multi-way intersections. The core problem in optimizing worst-case optimal plans is to pick an ordering of the query vertices to match. We design a cost-based optimizer that (i) picks efficient query vertex orderings for worst-case optimal plans; and (ii) generates
hybrid plans
that mix traditional binary joins with worst-case optimal style multiway intersections. Our cost metric combines the cost of binary joins with a new cost metric called
intersection-cost
. The plan space of our optimizer contains plans that are not in the plan spaces based on tree decompositions from prior work. In addition to our optimizer, we describe an
adaptive technique
that changes the orderings of the worst-case optimal subplans during query execution. We demonstrate the effectiveness of the plans our optimizer picks and the effectiveness of the adaptive technique through extensive experiments. Our optimizer is integrated into the Graphflow DBMS.
Cleanits
Authors: Ding Xiaoou , Wang Hongzhi , Su Jiaxuan , Li Zijue , Li Jianzhong , Gao Hong
Published: September 2019

The great amount of time series generated by machines has enormous value in intelligent industry. Knowledge can be discovered from high-quality time series, and used for production optimization and anomaly detection in industry. However, the original sensors data always contain many errors. This requires a sophisticated cleaning strategy and a well-designed system for industrial data cleaning. Motivated by this, we introduce Cleanits, a system for industrial time series cleaning. It implements an integrated cleaning strategy for detecting and repairing three kinds of errors in industrial time series. We develop reliable data cleaning algorithms, considering features of both industrial time series and domain knowledge. We demonstrate Cleanits with two real datasets from power plants. The system detects and repairs multiple dirty data precisely, and improves the quality of industrial time series effectively. Cleanits has a friendly interface for users, and result visualization along with logs are available during each cleaning process.
SystemER
Authors: Qian Kun , Popa Lucian , Sen Prithviraj
Published: September 2019

Entity Resolution (ER) is the task of identifying different representations of the same real-world object. To achieve scalability and the desired level of quality, the typical ER pipeline includes multiple steps that may involve low-level coding and extensive human labor. We present SystemER, a tool for learning explainable ER models that reduces the human labor all throughout the stages of the ER pipeline. SystemER achieves explainability by learning rules that not only perform a given ER task but are human-comprehensible; this provides transparency into the learning process, and further enables verification and customization of the learned model by the domain experts. By leveraging a human in the loop and active learning, SystemER also ensures that a small number of labeled examples is sufficient to learn high-quality ER models. SystemER is a full-fledged tool that includes an easy to use interface, support for both flat files and semi-structured data, and scale-out capabilities by distributing computation via Apache Spark.
CAPE
Authors: Miao Zhengjie , Zeng Qitian , Li Chenjie , Glavic Boris , Kennedy Oliver , Roy Sudeepa
Published: September 2019

In this demonstration we showcase Cape, a system that explains surprising aggregation outcomes. In contrast to previous work, which relies exclusively on provenance, Cape explains outliers in aggregation queries through related outliers in the opposite direction that provide
counterbalance
. The foundation of our approach are
aggregate regression patterns (ARPs)
that describe coarse-grained trends in the data. We define outliers as deviations from such patterns and present an efficient algorithm to find counterbalances explaining outliers. In the demonstration, the audience can run aggregation queries over real world datasets, identify outliers of interest in the result of such queries, and browse the patterns and explanations returned by Cape.
Flash in action
Authors: Sabek Ibrahim , Musleh Mashaal , Mokbel Mohamed F.
Published: September 2019

The current explosion in spatial data raises the need for efficient spatial analysis tools to extract useful information from such data. However, existing tools are neither generic nor scalable when dealing with big spatial data. This demo presents
Flash
; a framework for
generic
and
scalable
spatial data analysis, with a special focus on spatial probabilistic graphical modelling (SPGM).
Flash
exploits Markov Logic Networks (MLN) to express SPGM as a set of declarative logical rules. In addition, it provides spatial variations of the scalable RDBMS-based learning and inference techniques of MLN to efficiently perform SPGM predictions. To show
Flash
effectiveness, we demonstrate three applications that use
Flash
in their SPGM: (1) Bird monitoring, (2) Safety analysis, and (3) Land use change tracking.
SparkCruise
Authors: Roy Abhishek , Jindal Alekh , Patel Hiren , Gosalia Ashit , Krishnan Subru , Curino Carlo
Published: September 2019

Interactive data analytics is often inundated with common computations across multiple queries. These redundancies result in poor query performance and higher overall cost for the interactive query sessions. Obviously, reusing these common computations could lead to cost savings. However, it is difficult for the users to manually detect and reuse the common computations in their fast moving interactive sessions. In the paper, we propose to demonstrate SparkCruise, a computation reuse system that automatically selects the most useful common computations to materialize based on the past query workload. SparkCruise materializes these computations as part of query processing, so the users can continue with their query processing just as before and computation reuse is automatically applied in the background --- all without any modifications to the Spark code. We will invite the audience to play with several scenarios, such as workload redundancy insights and pay-as-you-go materialization, highlighting the utility of SparkCruise.