Why a Single Parallelization Strategy Is Not Enough in Knowledge Bases

We address the problem of parallelizing the evaluation of logic programs in data intensive applications. We argue that the appropriate parallelization strategy for logic-program evaluation depends on the program being evaluated. Therefore, this paper is concerned with the issues of program classification and parallelization strategies. We propose several parallelization strategies based on the concept of data reduction—the original logic program is evaluated by several processors working in parallel, each using only a subset of the database. The strategies differ on the evaluation cost, the overhead of communication and synchronization among processors, and the programs to which they are applicable. In particular, we start our study with pure parallelization, i.e., parallelization without overhead. An interesting class structure of logic programs is demonstrated, when considering amenability to pure parallelization. The relationship to the NC complexity class is demonstrated. Then we propose strategies that do incur an overhead, but are optimal in a sense that will be precisely defined. This paper makes the initial steps towards a theory of parallel logic programming

Authenticity and Provenance in Long Term Digital Preservation: Modeling and Implementation in Preservation Aware Storage

A growing amount of digital objects is designated for long term preservation- a time scale during which technologies, formats and communities are very likely to change. Specialized approaches, models and technologies are needed to guarantee the long-term understandability of the preserved data. Maintaining the authenticity (trustworthiness) and provenance (history of creation, ownership, accesses and changes) of the preserved objects for the long term is of great importance, since users must be confident that the objects in the changed environment are authentic. We present a novel model for managing authenticity in long term digital preservation systems and a supporting archival storage component. Th

Preservation datastores: Architecture for preservation aware storage

The volumes of digital infonnation are growing continuously and most of today's information is "born digital". Alongside this trend, business, scientific, artistic and cultural needs require much of this information to be kept for decades, centuries or longer. The convergence of these two trends implies the need for storage systems that support very long tenn preservation for digital information. We describe Preservation DataStores, a novel storage architecture to support digital preservation. It is a layered architecture that builds upon open standards, along with the OAlS, XAM and OSD standards. This new architecture transfonns the logical information-object, a basic concept in preservation systems, into a physical storage object. The transformation allows more robust and optimized implementations for preservation aware storage. The architecture of Preservation DataStores is being developed as all infrastructure component of the CASPAR project ' and will be tested in the context of this project using scientific, cultural, and artistic data. 'Work partially supported by European Community under the Information Society Technologies (1ST) program of the 6th FP for RTD- project CASPAR contract IST-033572. The authors are solely responsible for the content of this paper. It does not represent the opinion of the European Community, and the European Community is not responsible for any use that might be made of data appearing therein. 1

ENSURE: Long term digital preservation of Health Care, Clinical Trial and Financial data: Paper - iPres 2013 - Lisbon

This paper presents the initial results of the ENSURE (Enabling kNowledge Sustainability, Usability and Recovery for Economic value) project, which focuses on the challenges associated with the long-term preservation of data produced by organisations in the health care, clinical trials and financial sectors. In particular the project has looked at the economic implications of long-term preservation for business, how to maintain the accessibility and confidentiality of sensitive information in a changing environment, and how to detect and respond to such environmental changes. The project has developed a prototype system, which is based around a lifecycle manager and makes use of ontologies to identify and trigger necessary transformations of the data objects in order to ensure their long-term usability. It also uses cloud technology for its flexibility, expansibility, and low start-up costs. This paper presents one of the use cases: the health care as a way to illustrate some of the challenges addressed by the ENSURE system

Multimodal Prediction of Five-Year Breast Cancer Recurrence in Women Who Receive Neoadjuvant Chemotherapy

SIMPLE SUMMARY: An important clinical issue to consider when selecting neoadjuvant chemotherapy treatment for breast cancer is the likelihood of cancer recurrence. Accurately predicting the future outcome of the patient based on data available prior to treatment initiation could impact the treatment selection. We study a cohort of 1738 patients and explore the contribution of clinical history, immunohistochemical markers, and multiparametric magnetic resonance imaging to the prediction of post-treatment cancer recurrence. We analyzed this multimodal data using classical machine learning, image processing, and deep learning to increase the set of discriminating features. Our results demonstrate the ability to predict recurrence using each modality alone, and the possible improvement achieved by combining the modalities. We show that the models are especially accurate for differentiating specific groups of young women with poor prognoses. These methods were also used on a different dataset of 193 patients in an international challenge, where they won second place. ABSTRACT: In current clinical practice, it is difficult to predict whether a patient receiving neoadjuvant chemotherapy (NAC) for breast cancer is likely to encounter recurrence after treatment and have the cancer recur locally in the breast or in other areas of the body. We explore the use of clinical history, immunohistochemical markers, and multiparametric magnetic resonance imaging (DCE, ADC, Dixon) to predict the risk of post-treatment recurrence within five years. We performed a retrospective study on a cohort of 1738 patients from Institut Curie and analyzed the data using classical machine learning, image processing, and deep learning. Our results demonstrate the ability to predict recurrence prior to NAC treatment initiation using each modality alone, and the possible improvement achieved by combining the modalities. When evaluated on holdout data, the multimodal model achieved an AUC of 0.75 (CI: 0.70, 0.80) and 0.57 specificity at 0.90 sensitivity. We then stratified the data based on known prognostic biomarkers. We found that our models can provide accurate recurrence predictions (AUC > 0.89) for specific groups of women under 50 years old with poor prognoses. A version of our method won second place at the BMMR2 Challenge, with a very small margin from being first, and was a standout from the other challenge entries