Search

Your search keyword '"Herschel, Melanie"' showing total 173 results
Start Over Author "Herschel, Melanie"
173 results on '"Herschel, Melanie"'

2. From Plate to Prevention: A Dietary Nutrient-aided Platform for Health Promotion in Singapore

Author

Zheng, Kaiping, Nguyen, Thao, Chong, Jesslyn Hwei Sing, Goh, Charlene Enhui, Herschel, Melanie, Lee, Hee Hoon, Liu, Changshuo, Ooi, Beng Chin, Wang, Wei, and Yip, James

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Databases, Computer Science - Multimedia
Abstract

Singapore has been striving to improve the provision of healthcare services to her people. In this course, the government has taken note of the deficiency in regulating and supervising people's nutrient intake, which is identified as a contributing factor to the development of chronic diseases. Consequently, this issue has garnered significant attention. In this paper, we share our experience in addressing this issue and attaining medical-grade nutrient intake information to benefit Singaporeans in different aspects. To this end, we develop the FoodSG platform to incubate diverse healthcare-oriented applications as a service in Singapore, taking into account their shared requirements. We further identify the profound meaning of localized food datasets and systematically clean and curate a localized Singaporean food dataset FoodSG-233. To overcome the hurdle in recognition performance brought by Singaporean multifarious food dishes, we propose to integrate supervised contrastive learning into our food recognition model FoodSG-SCL for the intrinsic capability to mine hard positive/negative samples and therefore boost the accuracy. Through a comprehensive evaluation, we present performance results of the proposed model and insights on food-related healthcare applications. The FoodSG-233 dataset has been released in https://foodlg.comp.nus.edu.sg/.

Published
2023

3. Putting Co-Design-Supporting Data Lakes to the Test: An Evaluation on AEC Case Studies

Author

Herschel, Melanie, Gienger, Andreas, Lauer, Anja P. R., Stein, Charlotte, Skoury, Lior, Lässig, Nico, Ellwein, Carsten, Verl, Alexander, Wortmann, Thomas, Sauer, Cristina Tarin, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wrembel, Robert, editor, Chiusano, Silvia, editor, Kotsis, Gabriele, editor, Tjoa, A Min, editor, and Khalil, Ismail, editor

Published
2024
Full Text
View/download PDF

4. Knowledge-Infused Optimization for Parameter Selection in Numerical Simulations

Author

Meißner, Julia, Göddeke, Dominik, Herschel, Melanie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, De-Nian, editor, Xie, Xing, editor, Tseng, Vincent S., editor, Pei, Jian, editor, Huang, Jen-Wei, editor, and Lin, Jerry Chun-Wei, editor

Published
2024
Full Text
View/download PDF

6. To not miss the forest for the trees -- a holistic approach for explaining missing answers over nested data (extended version)

Author

Diestelkaemper, Ralf, Lee, Seokki, Herschel, Melanie, and Glavic, Boris

Subjects
Computer Science - Databases
Abstract

Query-based explanations for missing answers identify which operators of a query are responsible for the failure to return a missing answer of interest. This type of explanations has proven to be useful in a variety of contexts including debugging of complex analytical queries. Such queries are frequent in big data systems such as Apache Spark. We present a novel approach for producing query-based explanations. Our approach is the first to support nested data and to consider operators that modify the schema and structure of the data (e.g., nesting and projections) as potential causes of missing answers. To efficiently compute explanations, we propose a heuristic algorithm that applies two novel techniques: (i) reasoning about multiple schema alternatives for a query and (ii) re-validating at each step whether an intermediate result can contribute to the missing answer. Using an implementation of our approach on Spark, we demonstrate that it is the first to scale to large datasets and that it often finds explanations that existing techniques fail to identify., Comment: 21 pages

Published
2021

10. Distributed Tree-Pattern Matching in Big Data Analytics Systems

Author

Diestelkämper, Ralf, Herschel, Melanie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Darmont, Jérôme, editor, Novikov, Boris, editor, and Wrembel, Robert, editor

Published
2020
Full Text
View/download PDF

11. A System Framework for Personalized and Transparent Data-Driven Decisions

Author

Oppold, Sarah, Herschel, Melanie, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Dustdar, Schahram, editor, Yu, Eric, editor, Salinesi, Camille, editor, Rieu, Dominique, editor, and Pant, Vik, editor

Published
2020
Full Text
View/download PDF

14. Towards Integrating Collaborative Filtering in Visual Data Exploration Systems

Author

Lahmar, Houssem Ben, Herschel, Melanie, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Welzer, Tatjana, editor, Eder, Johann, editor, Podgorelec, Vili, editor, Wrembel, Robert, editor, Ivanović, Mirjana, editor, Gamper, Johann, editor, Morzy, Mikoƚaj, editor, Tzouramanis, Theodoros, editor, Darmont, Jérôme, editor, and Kamišalić Latifić, Aida, editor

Published
2019
Full Text
View/download PDF

17. Provenance for Entity Resolution

Author

Oppold, Sarah, Herschel, Melanie, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Belhajjame, Khalid, editor, Gehani, Ashish, editor, and Alper, Pinar, editor

Published
2018
Full Text
View/download PDF

20. LANCE: Piercing to the Heart of Instance Matching Tools

Author

Saveta, Tzanina, Daskalaki, Evangelia, Flouris, Giorgos, Fundulaki, Irini, Herschel, Melanie, Ngomo, Axel-Cyrille Ngonga, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Arenas, Marcelo, editor, Corcho, Oscar, editor, Simperl, Elena, editor, Strohmaier, Markus, editor, d'Aquin, Mathieu, editor, Srinivas, Kavitha, editor, Groth, Paul, editor, Dumontier, Michel, editor, Heflin, Jeff, editor, Thirunarayan, Krishnaprasad, editor, and Staab, Steffen, editor

Published
2015
Full Text
View/download PDF

26. Towards task-based parallelization for entity resolution

Author

Gazzarri, Leonardo and Herschel, Melanie

Abstract

Entity resolution (ER) refers to the problem of finding which virtual representations in one or more data sources refer to the same real-world entity. A central question in ER is how to find matching entity representations (so called duplicates) efficiently and in a scalable way. One general technique to address these issues is to leverage parallelization. In particular, almost all work on parallel ER focus on data parallelism. This paper focuses on task parallelism for ER. This type of parallelism allows to support incremental ER that offers incremental computation of the solution by streaming results of intermediate stages of ER as soon as they are computed. This possibly allows to obtain results in a more timely fashion and can also serve in a service-oriented setting with limited time or monetary budget. In summary, this paper presents a framework for task-parallelization of ER, supporting in particular ER of large amounts of semi-structured and heterogeneous data. We also discuss a possible implementation of our framework.

Published
2024
Full Text
View/download PDF

31. DyHealth

Author

Zheng, Kaiping, primary, Cai, Shaofeng, additional, Chua, Horng Ruey, additional, Herschel, Melanie, additional, Zhang, Meihui, additional, and Ooi, Beng Chin, additional

Published
2022
Full Text
View/download PDF

33. Experiences of pregnancy and perinatal healthcare access of women who are deaf: a qualitative study.

Author

Panko, Tiffany L., Cuculick, Jess, Albert, Sasha, Smith, Lauren D., Cooley, Margarita M., Herschel, Melanie, Mitra, Monika, and McKee, Michael

Subjects
MATERNITY nursing, HEALTH services accessibility, DEAF people, MEDICAL personnel, AMERICAN Sign Language, PREGNANCY
Abstract

Objective: Women who are deaf experience higher rates of reproductive healthcare barriers and adverse birth outcomes compared with their peers who can hear. This study explores the pregnancy experiences of women who are deaf to better understand their barriers to and facilitators of optimal pregnancy‐related health care. Design: Qualitative study using thematic analysis. Setting: Semi‐structured, individual, remote or in‐person interviews conducted in the USA. Sample Forty‐five women who are deaf and communicate using American Sign Language (ASL) and gave birth in the USA within the past 5 years participated in the interviews. Methods: Semi‐structured interviews explored how mothers who are deaf experienced pregnancy and birth, including access to perinatal information and resources, relationships with healthcare providers, communication access and their involvement with the healthcare system throughout pregnancy. A thematic analysis was conducted. Main outcome measures: Barriers and facilitators related to a positive experience of perinatal care access among women who are deaf. Results: Three major themes emerged: (1) communication accessibility; (2) communication satisfaction; and (3) healthcare provider and team support. Common barriers included choosing healthcare providers, inconsistent communication access and difficulty accessing health information. However, when women who are deaf were able to use ASL interpreters, they had more positive pregnancy and birth experiences. Self‐advocacy served as a common facilitator for more positive pregnancy and healthcare experiences. Conclusions: Healthcare providers need to be more aware of the communication and support needs of their patients who are deaf, especially how to communicate effectively. Increased cultural awareness and consistent provision of on‐site interpreters can improve pregnancy and birth experiences for women who are deaf. Linked article: This article is commented on by Moreland et al., pp.522–523 in this issue. To view this mini commentary visit https://doi.org/10.1111/1471-0528.17362 This paper has a Video Abstract presented by Jenna Stewardson, Deaf Health Laboratory Research Program Coordinator at the NTID Research Center on Culture and Language available at: https://vimeo.com/761853406. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

39. Using FALCES against bias in automated decisions by integrating fairness in dynamic model ensembles

Author

Lässig, Nico, Oppold, Sarah, and Herschel, Melanie

Subjects
Model fairness, model ensembles, bias in machine learning
Abstract

As regularly reported in the media, automated classifications and decisions based on machine learning models can cause unfair treatment of certain groups of a general population. Classically, the machine learning models are designed to make highly accurate decisions in general. When one machine learning model is not sufficient to define the possibly complex boundary between classes, multiple specialized" models are used within a model ensemble to further boost accuracy. In particular

Published
2021
Full Text
View/download PDF

40. Combining Programming-by-Example with Transformation Discovery from large Databases

Author

Sattler, Kai-Uwe, Herschel, Melanie, Lehner, Wolfgang, Özmen, Aslihan, Esmailoghli, Mahdi, Abedjan, Ziawasch, Sattler, Kai-Uwe, Herschel, Melanie, Lehner, Wolfgang, Özmen, Aslihan, Esmailoghli, Mahdi, and Abedjan, Ziawasch

Abstract

Data transformation discovery is one of the most tedious tasks in data preparation. In particular, the generation of transformation programs for semantic transformations is tricky because additional sources for look-up operations are necessary. Current systems for semantic transformation discovery face two major problems: either they follow a program synthesis approach that only scales to a small set of input tables, or they rely on extraction of transformation functions from large corpora, which requires the identification of exact transformations in those resources and is prone to noisy data. In this paper, we try to combine approaches to benefit from large corpora and the sophistication of program synthesis. To do so, we devise a retrieval and pruning strategy ensemble that extracts the most relevant tables for a given transformation task. The extracted resources can then be processed by a program synthesis engine to generate more accurate transformation results than state-of-the-art.

Published
2021

41. Silentium! Run-Analyse-Eradicate the Noise out of the DB/OS Stack

Author

Sattler, Kai-Uwe, Herschel, Melanie, Lehner, Wolfgang, Mauerer, Wolfgang, Ramsauer, Ralf, Edson, R. Lucas F., Lohmann, Daniel, Scherzinger, Stefanie, Sattler, Kai-Uwe, Herschel, Melanie, Lehner, Wolfgang, Mauerer, Wolfgang, Ramsauer, Ralf, Edson, R. Lucas F., Lohmann, Daniel, and Scherzinger, Stefanie

Abstract

When multiple tenants compete for resources, database performance tends to suffer. Yet there are scenarios where guaranteed sub-millisecond latencies are crucial, such as in real-time data processing, IoT devices, or when operating in safety-critical environments. In this paper, we study how to make query latencies deterministic in the face of noise (whether caused by other tenants or unrelated operating system tasks). We perform controlled experiments with an in-memory database engine in a multi-tenant setting, where we successively eradicate noisy interference from within the system software stack, to the point where the engine runs close to bare-metal on the underlying hardware. We show that we can achieve query latencies comparable to the database engine running as the sole tenant, but without noticeably impacting the workload of competing tenants. We discuss these results in the context of ongoing efforts to build custom operating systems for database workloads, and point out that for certain use cases, the margin for improvement is rather narrow. In fact, for scenarios like ours, existing operating systems might just be good enough, provided that they are expertly configured. We then critically discuss these findings in the light of a broader family of database systems (e.g. including disk-based), and how to extend the approach of this paper accordingly.

Published
2021

48. Efficient Window Aggregation with General Stream Slicing

Author

Traub, Jonas, Grulich, Philipp, Cuéllar, Alejandro Rodríguez, Breß, Sebastian, Katsifodimos, A, Rabl, Tilmann, Markl, Volker, Herschel, Melanie, Galhardas, Helena, Binnig, Carsten, Kaoudi, Zoi, Fundulaki, Irini, and Reinwald, Berthold

Abstract

Window aggregation is a core operation in data stream processing. Existing aggregation techniques focus on reducing latency, eliminating redundant computations, and minimizing memory usage. However, each technique operates under different assumptions with respect to workload characteristics such as properties of aggregation functions (e.g., invertible, associative), window types (e.g., sliding, sessions), windowing measures (e.g., time- or count-based), and stream (dis)order. Violating the assumptions of a technique can deem it unusable or drastically reduce its performance. In this paper, we present the first general stream slicing technique for window aggregation. General stream slicing automatically adapts to workload characteristics to improve performance without sacrificing its general applicability. As a prerequisite, we identify workload characteristics which affect the performance and applicability of aggregation techniques. Our experiments show that general stream slicing outperforms alternative concepts by up to one order of magnitude.

Published
2019

49. Demonstrating data collections curation and exploration with CURARE

Author

Vargas-Solar, Genoveva, Kemp, Gavin, Hernández-Gallegos, Irving, Espinosa Oviedo, J.A., Da Silva, Catarina Ferreira, Ghodous, Parisa, Herschel, Melanie, Galhardas, Helena, Reinwald, Berthold, Fundulaki, Irini, Binnig, Carsten, and Kaoudi, Zoi

Abstract

This paper demonstrates CURARE, an environment for curating and assisting data scientists to explore raw data collections. CURARE implements a data curation model used to store structural and quantitative metadata semi-automatically extracted. It provides associated functions for exploring these metadata. The demonstration proposed in this paper is devoted to evaluate and compare the effort invested by a data scientist when exploring data collections with and without CURARE assistance.

Published
2019

50. Discovering order dependencies through order compatibility

Author

Consonni, Cristian, Sottovia, Paolo, Montresor, Alberto, Velegrakis, Yannis, Kaoudi, Zoi, Galhardas, Helena, Fundulaki, Irini, Reinwald, Berthold, Herschel, Melanie, Binnig, Carsten, Sub Data Intensive Systems, and Data Intensive Systems

Subjects
Software, Information Systems, Computer Science Applications
Abstract

A relevant task in the exploration and understanding of large datasets is the discovery of hidden relationships in the data. In particular, functional dependencies have received considerable attention in the past. However, there are other kinds of relationships that are significant both for understanding the data and for performing query optimization. Order dependencies belong to this category. An order dependency states that if a table is ordered on a list of attributes, then it is also ordered on another list of attributes. The discovery of order dependencies has been only recently studied. In this paper, we propose a novel approach for discovering order dependencies in a given dataset. Our approach leverages the observation that discovering order dependencies can be guided by the discovery of a more specific form of dependencies called order compatibility dependencies. We show that our algorithm outperforms existing approaches on real datasets. Furthermore, our algorithm can be parallelized leading to further improvements when it is executed on multiple threads. We present several experiments that illustrate the effectiveness and efficiency of our proposal and discuss our findings.

Published
2019

Catalog

Books, media, physical & digital resources
See catalog results