bluuebunny/crossref_metadata_2025 huggingface dataset:
Punitive Scholarship
Authors: Urita Michiko
Published: September 2019

This article responds to Jeffrey Perl’s argument (in “Regarding Change at Ise Jingū,” Common Knowledge, Spring 2008) that, while there is a “paradigm shift” at Ise every twenty years, when the enshrined deity Amaterasu “shifts” from the current site to an adjacent one during the rite of shikinen sengū, the Jingū paradigm itself never changes and never ages. The author confirms Perl’s conclusion by examining the politicized scholarship, written since the 1970s, maintaining that Shinto is a faux religion invented prior to World War II as a means of unifying Japan behind government policies of ultranationalism and international expansion. This article shows, instead, how emperors—who are not political but religious figures in Japan—and the Jingū priesthood have acted together over the past thirteen hundred years to sustain the imperial shrine at Ise and its ancient rites. The so-called Meiji Restoration actually continued an imperial policy of restoring and intensifying the observance of Shinto rituals that were threatened by neglect. Meiji intervened personally in 1889 to ensure the continuity of hikyoku, an unvoiced and secret serenade to Amaterasu, by extending its venue from the imperial palace shrine to performance at Jingū as well. The author’s archival and ethnographic research at Ise and the National Archives shows how the arguments that Shinto is a modern invention are punitive rather than dispassionately historical.
The Latest Forms of Book-Burning
Authors: Tanselle G. Thomas
Published: September 2019

Current technological opportunities for preserving the texts of books, above all the microfilming process, have the unfortunate side effect, Tanselle argues, of destroying the books themselves. This modern form of book-burning is all the more unfortunate insofar as it is being pursued by many who would otherwise consider themselves advocates for books and reading. The principal mistake, which has guided public policy decisions in this process, is to elevate the “text” above the “book” and moreover above the experience of reading, which entails an encounter with a physical object. Such objects, furthermore, carry within them vast fields of historical information that are lost when we concern ourselves solely with the text and, indeed, can even lead to the loss of texts themselves. Despite the mass destruction that has already been carried out, it is not too late for librarians and large professional organizations to voice their concerns and shift attitudes away from the promise of future bookless libraries and a public no longer sensitized to the pleasures and importance of the physical aspects of reading.
Suffocation in the Polis
Authors: Perl Jeffrey M.
Published: September 2019

This introduction to the third and final part of the Common Knowledge symposium “Unsocial Thought, Uncommon Lives” (13:1 [Winter 2007]: 33–39) is reprinted here in a special issue of representative pieces from the journal’s first twenty-five years. The title is taken from an article by Isaiah Berlin in CK (7:3 [Winter 1998]: 186–214 and likewise reprinted in the anniversary issue). Perl’s essay argues against the Aristotelian (and, generally, commonsense) presumption that “man is a social animal” and explains that the CK symposium on unsocial thought was meant to substantiate that “societies do as a rule smother instinctive (along with distinctive) behaviors, but in the process they also as a rule incarnate the least respectable instincts with greater force than individuals can do independently. . . . If even heroism and altruism, let alone standard social conduct, are oblique expressions of aggression, cruelty, and the will to power (as the hermeneutics of suspicion maintains), then the obvious conclusion to reach is that human beings are not fit company for each other. . . . The standard means of veering off from this conclusion is to blame one’s own society, or aspects of contemporary society, and then to propose improvements. Historically, evidence suggests, efforts of this kind are (or else, become) opportunities for controversy and thus for exercise of the will to power. Social order is such that even the discussion of social order occasions conflict.” Perl goes on to argue that individualists and communitarians are “fundamentally in accord”: they are “teams” agreeing to the rules of a dubious conflict from which only “stylites, dendrites, and (on the hearty end of this spectrum) mendicants” have done what is required to exempt themselves.
The Political Economy of Hunger
Authors: Sen Amartya
Published: September 2019

Sen’s essay concerns the existence of extensive hunger amidst unprecedented global prosperity in the contemporary world, but he argues that the problem would be decisively solvable if our response were no longer shaped by Malthusian pessimism. Effective famine prevention does not turn on food supply per head and the automatic mechanism of the market: there can be plenty of food while large sections of the population lack the means to obtain it. Effective famine prevention thus requires “entitlements.” Economically, governments can and should provide public employment programs so that those threatened by famine can be empowered to command food. Politically, democratic participation and a free press can work to ensure government accountability for famine prevention. The choice that Sen urges, however, is not for the state over the market—the experience of the Indian state of Kerala demonstrates that a voluntaristic approach can work as well or better than China’s compulsory “one child policy” in limiting the rapid population growth that contributes to world hunger. Rather, a reasoned solution to the problem of hunger must acknowledge the complementary importance of both well-functioning markets and open and democratic public action.
Paediatric proton therapy
Authors: Thomas Heike , Timmermann Beate
Published: September 2019

Proton beam therapy is a highly conformal form of radiation therapy, which currently represents an important therapeutic component in multidisciplinary management in paediatric oncology. The precise adjustability of protons results in a reduction of radiation-related long-term side-effects and secondary malignancy induction, which is of particular importance for the quality of life. Proton irradiation has been shown to offer significant advantages over conventional photon-based radiotherapy, although the biological effectiveness of both irradiation modalities is comparable. This review evaluates current data from clinical and dosimetric studies on the treatment of tumours of the central nervous system, soft tissue and bone sarcomas of the head and neck region, paraspinal or pelvic region, and retinoblastoma. To date, the clinical results of irradiating childhood tumours with high-precision proton therapy are promising both with regard to tumour cure and the reduction of adverse events. Modern proton therapy techniques such as pencil beam scanning and intensity modulation are increasingly established modern facilities. However, further investigations with larger patient cohorts and longer follow-up periods are required, in order to be able to have clear evidence on clinical benefits.
Adrenal schwannoma: a case report
Authors: Dell'Aversano Orabona Giuseppina , Ricci Domenico , Emili Ilaria , Serpi Francesca , Ferrara Valerio , Vanzulli Angelo
Published: September 2019

Retroperitoneum is an uncommon site for Schwannoma tumors and among adrenal incidentaloma the Schwannoma is rare. This condition lacks of a specific clinical and radiological features, but correct diagnosis before pathological examination should be very important for clinical management and surgical decision. We describe a case of voluminous retroperitoneal incidental mass, that was proved to be an adrenal Schwannoma.
Open Problem—Size-Based Scheduling with Estimation Errors
Authors: Down Douglas G.
Published: September 2019

For queueing systems, leveraging knowledge of job sizes to perform size-based scheduling leads to policies with attractive performance characteristics. Although there is a body of literature in this area, in the interest of space, we highlight one classical result: for a single-server system, the shortest remaining processing time (SRPT) policy (priority is given to the job closest to completion) is known to minimize the mean response time ( Schrage and Miller 1966 ). Although this and related performance results have been known for some time, such size-based scheduling policies have not been deployed to any great extent in practice. One objection to their deployment is that the assumption that one knows job sizes exactly is problematic; the typical scenario would be that estimates of job sizes are available to make scheduling decisions. There is not a large literature on the study of queueing systems in which there are estimation errors for job sizes. In the next paragraph, we discuss some typical approaches and then conclude the section with two open problems of interest in this area.
Open Problem—Weakly Interacting Particle Systems on Dense Random Graphs
Authors: Wu Ruoyu
Published: September 2019

We first briefly introduce the following classic mean-field particle system given as a collection of [Formula: see text]-valued stochastic processes {[Formula: see text](t) : t ∈ [0, T][Formula: see text].[Formula: see text]Here, {Wi} are independent and identically distributed standard d-dimensional Brownian motions, and [Formula: see text] × [Formula: see text] → [Formula: see text] is assumed to be bounded and Lipschitz, but one could also allow for more general initial states, drift coefficient b, and a state-dependent diffusion coefficient.
Obesity surgery and Ehlers–Danlos syndrome: challenges and considerations based on a case report
Authors: Gerogiannis IN , Gkegkes ID , Dempster NJ , Gillies RS , Sgromo B
Published: September 2019

Ehlers–Danlos syndrome is a hereditary connective tissue disorder that has gastrointestinal manifestations in over 50% of its cases. We present the first case of bariatric surgery in a patient with Ehlers–Danlos syndrome and outline management challenges in the context of the relevant literature. A 56-year-old man with type IV Ehlers–Danlos syndrome and a body mass index of 41.8 kg/m2 was referred to the bariatric centre of the Churchill Hospital, Oxford, for consideration of surgery for morbid obesity. His comorbidity included type 2 diabetes, hypertension, dyslipidaemia and obstructive sleep apnoea. He underwent a laparoscopic Roux-en-Y gastric bypass. His initial recovery was uneventful and he was discharged on the first postoperative day. Six weeks later, he presented with 43.9% excess weight loss and improved glycaemic control. Three months postoperatively, however, he complained of dysphagia, regurgitation and postprandial pain. A barium meal and gastroscopy suggested the presence of a gastric diverticulum. A surgical exploration was planned. Intraoperative gastroscopy demonstrated an asymmetrical gastric pouch dilatation and the pouch was therefore refashioned laparoscopically. Despite the initial symptomatic relief, two months later he experienced retrosternal pain with progressive dysphagia. Since then, multiple endoscopic dilatations of the gastro-oesophageal junction have been performed for recurrence of symptoms. Finally, a laparoscopic hiatus hernia repair and adhesiolysis was performed resulting in complete relief of patient’s symptoms. Bariatric management of patients with Ehlers–Danlos syndrome can prove challenging. The bariatric team must implement a careful management plan including a detailed consent process, a tailored surgical intervention and a follow-up focused on potential gastrointestinal manifestations.
A new epistemology of evidence-based policy
Authors: Sayer Philip
Published: September 2019

In a remarkably short period, ‘evidence-based policy’ (EBP), and the associated discourses of ‘what works’, have risen to prominence as a set of organising principles for public policy decision-making. Critics of EBP frequently point to its implicit positivist assumptions by highlighting the socially constructed nature of evidence. However, the effectiveness of this critique is limited by the imprecise and often pejorative use of the term ‘positivism’. This article therefore seeks to offer a more precise account of the underlying assumptions of EBP. To do so, it draws on an epistemological position known as process reliabilism, which analyses the justification of a belief by assessing whether it has been reached by means of an epistemically reliable decision-making process or processes. Through this framework, the article advocates a new approach to EBP which is framed around the principle of avoiding error, rather than that of seeking truth.
The expertise of politicians and their role in epistemic communities
Authors: Grødem Anne Skevik , Hippe Jon M.
Published: September 2019

Existing studies often regard politicians as outside epistemic communities, owing to a lack of policy or technical expertise. In contrast, this article argues that politicians can also be experts, and can play a pivotal role within existing epistemic communities. Specifically, it finds that this expertise is acquired through two distinct, but interrelated, processes. First, politicians who work on an issue for a sustained period and have access to expert knowledge can be recruited to epistemic communities. Second, politicians with a relevant academic or professional background can become allies for such communities. This argument is developed through an analysis of pension reform in Norway. Drawing on interviews with key policy actors, we demonstrate that civil servants in the Ministry of Finance constituted an epistemic community which recruited key politicians through the Pension Commission and benefited from having an economist and ally as Prime Minister.
Opportunities for youth participatory action research to inform school district decisions
Authors: Cohen Alison K. , Ozer Emily J. , Abraczinskas Michelle , Voight Adam , Kirshner Ben , Devinney Molly
Published: September 2019

Background

Youth participatory action research (YPAR) is an equity-focused approach intended to generate local knowledge and democratise the production of research evidence.

Aims/objectives

We explore the promise and challenges of YPAR to inform education policy decision making. We focus on California’s Local Control Funding Formula (LCFF)’s Local Control and Accountability Plan (LCAP) initiative, which requires districts to engage with diverse stakeholders to make decisions. We provide a case example of California’s Stockton Unified School District. Stockton currently uses the Peer Leaders Uniting Students’s YPAR curriculum to inform their LCAP work.

Key conclusions

YPAR offers opportunities for new insights, and can be implemented successfully at scale. While Stockton was doing YPAR before LCFF and LCAP existed, they enabled Stockton to expand its YPAR programming, with the goal of using YPAR evidence to provide useful information for educational decision making, policies, and programmes. For example, one YPAR project focused on student tardiness and, using data from multiple sources, proposed lengthening the passing period so that it was physically possible to walk to class in the time permitted. Here, YPAR supports those people most affected by education policy – the students – to have the power to inform decisions that affect them. YPAR can broaden the set of evidence and perspectives that decision makers review to inform policy decisions. We encourage researchers and practitioners to study and create policy structures that support using YPAR to inform policy. We also encourage policy makers to develop more policies that can facilitate the use of YPAR, in education and beyond.
For better or for worse: does the UK means-tested social security system encourage partnership dissolution?
Authors: Griffiths Rita
Published: September 2019

Unemployed and low-income couples entitled to means-tested benefits are known to have higher rates of separation and divorce than couples in which one or both partners are in regular, paid work. However, how and why unemployment and benefit receipt increases the risk of partnership dissolution remains the subject of much debate. In recent policy discourse, financial differentials in benefit entitlement between lone and couple parents are said to encourage intact couples to separate. Based on in-depth, face-to-face interviews with a group of low-income mothers who had been partnered prior to claiming lone parent benefits, this paper explores whether benefit entitlement or receipt influenced the decision to separate or divorce. The research found that more salient to partnership dissolution than the amount of benefits a couple may have been entitled to, was who had access to the money, how it was managed and how it was spent. To the extent that welfare systems influence which member of a couple has access to household income, the design and administration of benefits was having an important contributory effect. Policy implications of paying Universal Credit to couples in the form of a single monthly household award into one bank account are discussed.
Anticancer activities of toxic isolate of Xestospongia testudinaria sponge
Authors: Swantara Made Dira , Rita Wiwik Susanah , Suartha Nyoman , Agustina Kadek Karang
Published: September 2019

Aim: The purposes of this study were to determine the anticancer activity of Xestospongia testudinaria sponge isolate and identify the responsible compounds.

Materials and Methods: The metabolites were extracted using methanol maceration at room temperature. The separation and purification of metabolites were performed using fractionation and column chromatography. The toxicity was examined using the brine shrimp lethality assay, and the toxic isolates were tested for anticancer activity against HeLa cells. Gas chromatography-mass spectrometry analysis was used to identify the compounds in the isolate.

Results: When the methanol extract was partitioned with n-hexane, chloroform, and n-butanol, the chloroform fraction was the most toxic, with a concentration that results in 50% lethality (LC50) value of 39.81 ppm. After separation of the chloroform extract, fraction B (FB) was the most toxic, with an LC50 value of 44.67 ppm. The isolate from FB showed anticancer activity with a concentration at which 50% of growth was inhibited (IC50) of 2.273 ppm. In total, 21 compounds were identified in anticancer isolates: Nonanedioic acid; tetradecanoic acid; trans-phytol; 2-pentadecanone- 6,10,14-trimethyl; pentadecanoic acid; 2-hexadecen-1-ol, 3,7,11,15-tetramethyl-; pentadecanoic acid; 2-hexadecen-1-ol, 3,7,11,15-tetramethyl-; 2,3,7-trimethyloctanal; palmitic acid; docosanoic acid, ethyl ester; 1,E-11,Z-13-octadecatriene; chloromethyl 4-chlorododecanoate; 1-tricosene; 9,12-octadecadienoic acid; 4,8,12,16-tetramethylheptadecan-4-olide; 1-docosene; heneicosane; phosphonic acid, dioctadecyl ester; dodecane,4,6-dimethyl-; n-tetratriacontane; 1-iodohexadecane; and n-heneicosane.

Conclusion: These findings indicate that the isolate of X. testudinaria can be used as a natural anticancer toward HeLa cell.
Conflicto armado y victimización de género en Colombia
Authors: Salcedo Ávila Edwin Diego , Paes-Machado Eduardo
Published: September 2019

Este trabajo discute la victimización de mujeres en el conflicto armado colombiano. Utiliza datos de entrevistas con mujeres desplazadas de las zonas de conflicto. Analiza la victimización por pérdidas materiales, violencia sexual, asesinatos y desaparición de personas más allegadas. Muestra que a pesar de la violencia sexual ser la forma más específica de victimización de las mujeres, las demás victimizaciones también tuvieron una fuerte incidencia sobre ellas. Mientras la destrucción del patrimonio material minó la reproducción social de sus grupos familiares, las pérdidas efectivas y potenciales de personas significativas afectaron los lazos de maternidad, cuidado y protección por parte de ellas. Afirma que la sobrecarga de ahí resultante obligó a estas mujeres a reaccionar frente a los grupos armados y huir de sus lugares de origen para proteger sus familias. Concluye que los efectos asociados al desplazamiento forzado garantizan que el peso de las victimizaciones sea sentido por más tiempo, acompañándolas y proyectándose en sus nuevos modos de vida en los lugares de destino.
Orden y castigo. Didier Fassin y su estudio sobre policías y castigos
Authors: Galvani Mariana , Mouzo Karina
Published: September 2019

En el presente texto realizamos un recorrido posible por dos libros de la obra de Didier Fassin. Nos referimos a Las fuerzas del orden Una etnografía del accionar policial en las periferias urbanas y a Castigar. Una pasión contemporánea. Más allá de los distintos enfoques asumidos en cada uno de ellos nos propusimos abordarlos en conjunto para de ese modo ver algunos elementos que nos parecen interesantes para enriquecer la mirada de aquellos que, desde las ciencias sociales, nos proponemos estudiar el castigo y el accionar de las fuerzas de seguridad.
Online template induction for machine-generated emails
Authors: Whittaker Michael , Edmonds Nick , Tata Sandeep , Wendt James B. , Najork Marc
Published: September 2019

In emails, information abounds. Whether it be a bill reminder, a hotel confirmation, or a shipping notification, our emails contain useful bits of information that enable a number of applications. Most of this email traffic is machine-generated, sent from a business to a human. These business-to-consumer emails are typically instantiated from a set of email templates, and discovering these templates is a key step in enabling a variety of intelligent experiences. Existing email information extraction systems typically separate information extraction into two steps: an
offline
template discovery process (called template induction) that is periodically run on a sample of emails, and an
online
email annotation process that applies discovered templates to emails as they arrive. Since information extraction requires an email's template to be known, any delay in discovering a newly created template causes missed extractions, lowering the overall extraction coverage. In this paper, we present a novel system called Crusher that discovers templates completely online, reducing template discovery delay from a week (for the existing MapReduce-based batch system) to minutes. Furthermore, Crusher has a resource consumption footprint that is significantly smaller than the existing batch system. We also report on the surprising lesson we learned that conventional stream processing systems do
not
present a good framework on which to build Crusher. Crusher delivers an order of magnitude more throughput than a prototype built using a stream processing engine. We hope that these lessons help designers of stream processing systems accommodate a broader range of applications like online template induction in the future.
Blockchain meets database
Authors: Nathan Senthil , Govindarajan Chander , Saraf Adarsh , Sethi Manish , Jayachandran Praveen
Published: September 2019

In this paper, we design and implement the first-ever decentralized replicated relational database with blockchain properties that we term
blockchain relational database
. We highlight several similarities between features provided by blockchain platforms and a replicated relational database, although they are conceptually different, primarily in their trust model. Motivated by this, we leverage the rich features, decades of research and optimization, and available tooling in relational databases to build a blockchain relational database. We consider a permissioned blockchain model of known, but mutually distrustful organizations each operating their own database instance that are replicas of one another. The replicas execute transactions independently and engage in decentralized consensus to determine the commit order for transactions. We design two approaches, the first where the commit order for transactions is agreed upon prior to executing them, and the second where transactions are executed without prior knowledge of the commit order while the ordering happens in parallel. We leverage serializable snapshot isolation (SSI) to guarantee that the replicas across nodes remain consistent and respect the ordering determined by consensus, and devise a new variant of SSI based on block height for the latter approach. We implement our system on PostgreSQL and present detailed performance experiments analyzing both approaches.
An intermediate representation for optimizing machine learning pipelines
Authors: Kunft Andreas , Katsifodimos Asterios , Schelter Sebastian , Breß Sebastian , Rabl Tilmann , Markl Volker
Published: September 2019

Machine learning (ML) pipelines for model training and validation typically include preprocessing, such as data cleaning and feature engineering, prior to training an ML model. Preprocessing combines relational algebra and user-defined functions (UDFs), while model training uses iterations and linear algebra. Current systems are tailored to either of the two. As a consequence, preprocessing and ML steps are optimized in isolation. To enable holistic optimization of ML training pipelines, we present Lara, a declarative domain-specific language for collections and matrices. Lara's inter-mediate representation (IR) reflects on the complete program, i.e., UDFs, control flow, and both data types. Two views on the IR enable diverse optimizations. Monads enable operator pushdown and fusion across type and loop boundaries. Combinators provide the semantics of domain-specific operators and optimize data access and cross-validation of ML algorithms. Our experiments on preprocessing pipelines and selected ML algorithms show the effects of our proposed optimizations on dense and sparse data, which achieve speedups of up to an order of magnitude.
Distributed implementations of dependency discovery algorithms
Authors: Saxena Hemant , Golab Lukasz , Ilyas Ihab F.
Published: September 2019

We analyze the problem of discovering dependencies from distributed big data. Existing (non-distributed) algorithms focus on minimizing computation by pruning the search space of possible dependencies. However, distributed algorithms must also optimize communication costs, especially in shared-nothing settings, leading to a more complex optimization space. To understand this space, we introduce six primitives shared by existing dependency discovery algorithms, corresponding to data processing steps separated by communication barriers. Through case studies, we show how the primitives allow us to analyze the design space and develop communication-optimized implementations. Finally, we support our analysis with an experimental evaluation on real datasets.
SLOG
Authors: Ren Kun , Li Dennis , Abadi Daniel J.
Published: September 2019

For decades, applications deployed on a world-wide scale have been forced to give up at least one of (1) strict serializability (2) low latency writes (3) high transactional throughput. In this paper we discuss SLOG: a system that avoids this tradeoff for workloads which contain physical region locality in data access. SLOG achieves high-throughput, strictly serializable ACID transactions at geo-replicated distance and scale for all transactions submitted across the world, all the while achieving low latency for transactions that initiate from a location close to the home region for data they access. Experiments find that SLOG can reduce latency by more than an order of magnitude relative to state-of-the-art strictly serializable geo-replicated database systems such as Spanner and Calvin, while maintaining high throughput under contention.
A query system for efficiently investigating complex attack behaviors for enterprise security
Authors: Gao Peng , Xiao Xusheng , Li Zhichun , Jee Kangkook , Xu Fengyuan , Kulkarni Sanjeev R. , Mittal Prateek
Published: September 2019

The need for countering Advanced Persistent Threat (APT) attacks has led to the solutions that ubiquitously monitor system activities in each enterprise host, and perform timely attack investigation over the monitoring data for uncovering the attack sequence. However, existing general-purpose query systems lack explicit language constructs for expressing key properties of major attack behaviors, and their semantics-agnostic design often produces inefficient execution plans for queries. To address these limitations, we build Aiql, a novel query system that is designed with novel types of domain-specific optimizations to enable efficient attack investigation. Aiql provides (1) a domain-specific data model and storage for storing the massive system monitoring data, (2) a domain-specific query language,
Attack Investigation Query Language
(Aiql) that integrates critical primitives for expressing major attack behaviors, and (3) an optimized query engine based on the characteristics of the data and the semantics of the query to efficiently schedule the execution. We have deployed Aiql in NEC Labs America comprising 150 hosts. In our demo, we aim to show the complete usage scenario of Aiql by (1) performing an APT attack in a controlled environment, and (2) using Aiql to investigate such attack by querying the collected system monitoring data that contains the attack traces. The audience will have the option to perform the APT attack themselves under our guidance, and interact with the system and investigate the attack via issuing queries and checking the query results through our web UI.
NashDB
Authors: Marcus Ryan , Zhang Chi , Yu Shuai , Kao Geoffrey , Papaemmanouil Olga
Published: September 2019

Modern elastic computing systems allow applications to scale up and down automatically, increasing capacity for workload spikes and ensuring cost savings during lulls in activity. Adapting database management systems to work on top of such elastic infrastructure is not a trivial task, and requires a deep understanding of the sophisticated interplay between data fragmentation, replica allocation, and cluster provisioning. This demonstration showcases NashDB, an end-to-end method for addressing these concerns in an automatic way. NashDB relies on economic models to maximize query performance while staying within a user's budget. This demonstration will (1) allow audience members to see how NashDB handles shifting workloads in an adaptive way, and (2) allow audience members to test NashDB themselves by constructing synthetic workloads and seeing how NashDB adapts a cluster to them in real time.
I can't believe it's not (only) software!
Authors: Kuhring Lucas , István Zsolt
Published: September 2019

There is a steady increase in the size of data stored and processed as part of data science applications, leading to bottlenecks and inefficiencies at various layers of the stack. One way of reducing such bottlenecks and increasing energy efficiency is by tailoring the underlying distributed storage solution to the application domain, using resources more efficiently. We explore this idea in the context of a popular column-oriented storage format used in big data workloads, namely Apache Parquet.

Our prototype uses an FPGA-based storage node that offers high bandwidth data deduplication and a companion software library that exposes an API for Parquet file access. This way the storage node remains general purpose and could be shared by applications from different domains, while, at the same time, benefiting from deduplication well suited to Apache Parquet files and from selective reads of columns in the file.

In this demonstration we show, on the one hand, that by relying on the FPGA's dataflow processing model, it is possible to implement in-line deduplication without increasing latencies significantly or reducing throughput. On the other hand, we highlight the benefits of implementing the application-specific aspects in a software library instead of FPGA circuits and how this enables, for instance, regular data science frameworks running in Python to access the data on the storage node and to offload filtering operations.
DPSAaS
Authors: Xu Min , Wang Tianhao , Ding Bolin , Zhou Jingren , Hong Cheng , Huang Zhicong
Published: September 2019

Differential privacy has emerged as the
de facto
standard for privacy definitions, and been used by,
e.g
., Apple, Google, Uber, and Microsoft, to collect sensitive information about users and to build privacy-preserving analytics engines. However, most of such advanced privacy-protection techniques are not accessible to mid-size companies and app developers in the cloud. We demonstrate a lightweight middleware
DPSAaS
, which provides
d
ifferentially
p
rivate data-sharing-and-analytics functionality
a
s cloud services.

We focus on multi-dimensional analytical (MDA) queries under local differential privacy (LDP) in this demo. MDA queries against a fact table have predicates on (categorical or ordinal) dimensions and aggregate one or more measures. In the absence of a trusted agent, sensitive dimensions and measures are encoded in a privacy-preserving way locally using our LDP data sharing service, before being sent to the data collector. The data collector estimates the answers to MDA queries from the encoded data, using our data analytics service. We will highlight the design decisions of DPSAaS and twists made to LDA algorithms to fit the design, in order to smoothly connect DPSAaS to the data processing platform and analytics engines, and to facilitate efficient large-scale processing.
Datalignment
Authors: Deutch Daniel , Marants Evgeny , Moskovitch Yuval
Published: September 2019

We focus on the problem of aligning ontology relations, namely finding relation names that correspond to the same or related concepts. Such alignment is a prerequisite to the integration of the multiple available Knowledge Bases many of which include similar concepts, differently termed. We propose a novel approach for this problem, by leveraging association rules - originally mined in order to enrich the ontological content. Here, we treat the rules as Datalog programs and look for bounded-depth sub-programs that are contained in (or equivalent to) each other. Heads of such programs intuitively correspond to related concepts, and we propose them as candidates for alignment. The candidate alignments require further verification by experts; to this end we accompany each aligned pair with explanations based on the provenance of each relation according to its sub-program. We have implemented our novel solution in a system called Datalignment. We propose to demonstrate Datalignment, presenting the aligned pairs that it finds, and the computed explanations, in context of real-life Knowledge Bases.
Juneau
Authors: Zhang Yi , Ives Zachary G.
Published: September 2019

In collaborative settings such as multi-investigator laboratories, data scientists need improved tools to manage not their data
records
but rather their
data sets and data products
, to facilitate both provenance tracking and data (and code) reuse within their data lakes and file systems. We demonstrate the Juneau System, which extends computational notebook software (Jupyter Notebook) as an instrumentation and data management point for overseeing and facilitating improved dataset usage, through capabilities for indexing, searching, and recommending "complementary" data sources, previously extracted machine learning features, and additional training data. This demonstration focuses on how we help the user find related datasets via
search
.
PSynDB
Authors: Huang Zhiqi , McKenna Ryan , Bissias George , Miklau Gerome , Hay Michael , Machanavajjhala Ashwin
Published: September 2019

Across many application domains, trusted parties who collect sensitive information need mechanisms to safely disseminate data. A favored approach is to generate
synthetic data
: a dataset similar to the original, hopefully retaining its statistical features, but one that does not reveal the private information of contributors to the data.

We present PSynDB, a web-based synthetic table generator that is built on recent privacy technologies [10,11,15]. PSynDB satisfies the formal guarantee of differential privacy and generates synthetic tables with high accuracy for tasks that the user specifies as important. PSynDB allows users to browse expected error rates before running the mechanism, a useful feature for making important policy decisions, such as setting the privacy loss budget. When the user has finished configuration, the tool outputs a data synthesis program that can be ported to a trusted environment. There it can be safely executed on the private data to produce the private synthetic dataset for broad dissemination.
Making an RDBMS data scientist friendly
Authors: D'silva Joseph Vinish , De Moor Florestan , Kemme Bettina
Published: September 2019

We are currently witnessing the rapid evolution and adoption of various data science frameworks that function external to the database. Any support from conventional RDBMS implementations for data science applications has been limited to procedural paradigms such as user-defined functions (UDFs) that lack exploratory programming support. Therefore, the current status quo is that during the exploratory phase, data scientists usually use the database system as the "data storage" layer of the data science framework, whereby the majority of computation and analysis is performed outside the database, e.g., at the client node. We demonstrate AIDA, an in-database framework for data scientists. AIDA allows users to write interactive Python code using a development environment such as a Jupyter notebook. The actual execution itself takes place inside the database (near-data), where a server component of AIDA, that resides inside the embedded Python interpreter of the RDBMS, manages the data sets and computations. The demonstration will also show the visualization capabilities of AIDA where the progress of computation can be observed through live updates. Our evaluations show that AIDA performs several times faster compared to contemporary external data science frameworks, but is much easier to use for exploratory development compared to database UDFs.
AggChecker
Authors: Jo Saehan , Trummer Immanuel , Yu Weicheng , Wang Xuezhi , Yu Cong , Liu Daniel , Mehta Niyati
Published: September 2019

We demonstrate AggChecker, a novel tool for verifying textual summaries of relational data sets. The system automatically verifies natural language claims about numerical aggregates against the underlying raw data. The system incorporates a combination of natural language processing, information retrieval, machine learning, and efficient query processing strategies. Each claim is translated into a semantically equivalent SQL query and evaluated against the database. Our primary goal is analogous to that of a spell-checker: to identify erroneous claims and provide guidance in correcting them. In this demonstration, we show that our system enables users to verify text summaries much more efficiently than a standard SQL interface.
PivotE
Authors: Han Xueran , Chen Jun , Lu Jiaheng , Chen Yueguo , Du Xiaoyong
Published: September 2019

A Web-scale knowledge graph (KG) typically contains millions of entities and thousands of entity types. Due to the lack of a pre-defined data schema such as the ER model, entities in KGs are loosely coupled based on their relationships, which brings challenges for effective accesses of the KGs in a structured manner like SPARQL. This demonstration presents an entity-oriented exploratory search prototype system that is able to support search and explore KGs in a exploratory search manner, where local structures of KGs can be dynamically discovered and utilized for guiding users. The system applies a path-based ranking method for recommending similar entities and their relevant information as exploration pointers. The interface is designed to assist users to investigate a domain (particular type) of entities, as well as to explore the knowledge graphs in various relevant domains. The queries are dynamically formulated by tracing the users' dynamic clicking (exploration) behaviors.

In this demonstration, we will show how our system visualize the underlying entity structures, as well as explain the semantic correlations among them in a unified interface, which not only assist users to learn about the properties of entities in many aspects but also guide them to further explore the information space.
The ever evolving online labor market
Authors: Amer-Yahia Sihem , Roy Senjuti Basu
Published: September 2019

The goal of this tutorial is to make the audience aware of various discipline-specific research activities that could be characterized to be part of online labor markets and advocate for a unified framework that is interdisciplinary in nature and requires convergence of different research disciplines. We will discuss how such a framework could bring transformative effect on the nexus of humans, technology, and the future of work.
TitAnt
Authors: Cao Shaosheng , Yang XinXing , Chen Cen , Zhou Jun , Li Xiaolong , Qi Yuan
Published: September 2019

With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business. To tackle this problem, we introduce the TitAnt, a transaction fraud detection system deployed in Ant Financial, one of the largest Fintech companies in the world. The system is able to predict online real-time transaction fraud in mere milliseconds. We present the problem definition, feature extraction, detection methods, implementation and deployment of the system, as well as empirical effectiveness. Extensive experiments have been conducted on large real-world transaction data to show the effectiveness and the efficiency of the proposed system.
Customizable and scalable fuzzy join for big data
Authors: Chen Zhimin , Wang Yue , Narasayya Vivek , Chaudhuri Surajit
Published: September 2019

Fuzzy join is an important primitive for data cleaning. The ability to customize fuzzy join is crucial to allow applications to address domain-specific data quality issues such as synonyms and abbreviations. While efficient indexing techniques exist for single-node implementations of customizable fuzzy join, the state-of-the-art scale-out techniques do not support customization, and exhibit poor performance and scalability characteristics. We describe the design of a scale-out fuzzy join operator that supports customization. We use a locality-sensitive-hashing (LSH) based signature scheme, and introduce optimizations that result in significant speed up with negligible impact on recall. We evaluate our implementation on the Azure Databricks version of Spark using several real-world and synthetic data sets. We observe speedups exceeding 50X compared to the best-known prior scale-out technique, and close to linear scalability with data size and number of nodes.
Yugong
Authors: Huang Yuzhen , Shi Yingjie , Zhong Zheng , Feng Yihui , Cheng James , Li Jiwei , Fan Haochuan , Li Chao , Guan Tao , Zhou Jingren
Published: September 2019

Companies like Alibaba operate tens of data centers (DCs) across geographically distributed locations. These DCs collectively provide the storage space and computing power for the company, storing EBs of data and serving millions of batch analytics jobs every day. In Alibaba, as our businesses grow, there are more and more cross-DC dependencies caused by jobs reading data from remote DCs. Consequently, the precious wide area network bandwidth becomes a major bottleneck for operating geo-distributed DCs at scale. In this paper, we present Yugong --- a system that manages data placement and job placement in Alibaba's geo-distributed DCs, with the objective to minimize cross-DC bandwidth usage. Yugong uses three methods, namely project placement, table replication, and job outsourcing, to address the issues of high bandwidth consumption across the DCs. We give the details of Yugong's design and implementation for the three methods, and describe how it cooperates with other systems (e.g., Alibaba's big data analytics platform and cluster scheduler) to improve the productivity of the DCs. We also report comprehensive performance evaluation results, which validate the design of Yugong and show that significant reduction in cross-DC bandwidth usage has been achieved.
A distributed system for large-scale n-gram language models at Tencent
Authors: Long Qiang , Wang Wei , Deng Jinfu , Liu Song , Huang Wenhao , Chen Fangying , Liu Sifan
Published: September 2019

n-gram language models are widely used in language processing applications, e.g., automatic speech recognition, for ranking the candidate word sequences generated from the generator model, e.g., the acoustic model. Large n-gram models typically give good ranking results; however, they require a huge amount of memory storage. While distributing the model across multiple nodes resolves the memory issue, it nonetheless incurs a great network communication overhead and introduces a different bottleneck. In this paper, we present our distributed system developed at Tencent with novel optimization techniques for reducing the network overhead, including distributed indexing, batching and caching. They reduce the network requests and accelerate the operation on each single node. We also propose a cascade fault-tolerance mechanism which adaptively switches to small n-gram models depending on the severity of the failure. Experimental study on 9 automatic speech recognition (ASR) datasets confirms that our distributed system scales to large models efficiently, effectively and robustly. We have successfully deployed it for Tencent's WeChat ASR with the peak network traffic at the scale of 100 millions of messages per minute.
Couchbase analytics
Authors: Hubail Murtadha AI , Alsuliman Ali , Blow Michael , Carey Michael , Lychagin Dmitry , Maxon Ian , Westmann Till
Published: September 2019

Couchbase Server is a highly scalable document-oriented database management system. With a shared-nothing architecture, it exposes a fast key-value store with a managed cache for sub-millisecond data operations, indexing for fast queries, and a powerful query engine for executing declarative SQL-like queries. Its Query Service debuted several years ago and supports high volumes of low-latency queries and updates for JSON documents. Its recently introduced Analytics Service complements the Query Service. Couchbase Analytics, the focus of this paper, supports complex analytical queries (e.g., ad hoc joins and aggregations) over large collections of JSON documents. This paper describes the Analytics Service from the outside in, including its user model, its SQL++ based query language, and its MPP-based storage and query processing architecture. It also briefly touches on the relationship of Couchbase Analytics to Apache AsterixDB, the open source Big Data management system at the core of Couchbase Analytics.
Performance in the spotlight
Authors: Colyer Adrian
Published: September 2019

Performance in its various guises features prominently in research evaluations, and rightly so. Without adequate performance a system is not fit for purpose. That doesn't necessarily mean we should pursue performance at all costs though. In this talk we'll explore a variety of additional evaluation criteria, with a focus on those that are most important to practitioners, and ask whether or not considering them can open up interesting avenues of research.