Data quality affects machine learning (ML) model performances, and data scientists spend considerable amount of time on data cleaning before model training. However, to date, there does not exist a rigorous study on how exactly cleaning affects ML - ML community usually focuses on developing ML algorithms that are robust to some particular noise types of certain distributions, while database (DB) community has been mostly studying the problem of data cleaning alone without considering how data is consumed by downstream ML analytics.We propose a CleanML study that systematically investigates the impact of data cleaning on ML classification tasks. The open-source and extensible CleanML study currently includes 14 real-world datasets with real errors, five common error types, seven different ML models, and multiple cleaning algorithms for each error type (including both commonly used algorithms in practice as well as state-of-the-art solutions in academic literature). We control the randomness in ML experiments using statistical hypothesis testing, and we also control false discovery rate in our experiments using the Benjamini-Yekutieli (BY) procedure. We analyze the results in a systematic way to derive many interesting and nontrivial observations. We also put forward multiple research directions for researchers.

The design of expressive representations of entities and relations in a knowledge graph is an important endeavor.While many of the existing approaches have primarily focused on learning from relational patterns and structural information, the intrinsic complexity of KG entities has been more or less overlooked.More concretely, we hypothesize KG entities may be more complex than we think, i.e., an entity may wear many hats and relational triplets may form due to more than a single reason.To this end, this paper proposes to learn disentangled representations of KG entities -a new method that disentangles the inner latent properties of KG entities.Our disentangled process operates at the graph level and a neighborhood mechanism is leveraged to disentangle the hidden properties of each entity.This disentangled representation learning approach is model agnostic and compatible with canonical KG embedding approaches.We conduct extensive experiments on several benchmark datasets, equipping a variety of models (DistMult, SimplE, and QuatE) with our proposed disentangling mechanism.Experimental results demonstrate that our proposed approach substantially improves performance on key metrics.

Abstract In this paper we propose a peer‐to‐peer (P2P) Grid comprising resources such as relatively static clients, high‐end resources and a dynamic collection of multiple P2P subsystems. We investigate the architecture of the messaging and event service that will support such a hybrid environment. We designed a distributed publish–subscribe system NaradaBrokering for XML‐specified messages. NaradaBrokering provides support for centralized, distributed and P2P (via JXTA) interactions. Here we investigate and present our strategy for the integration of JXTA into NaradaBrokering. The resultant system naturally scales with multiple Peer Groups linked by NaradaBrokering. Copyright © 2005 John Wiley & Sons, Ltd.

Abstract In this paper we present the results pertaining to the NaradaBrokering middleware infrastructure. NaradaBrokering is designed to run on a large network of cooperating broker nodes. NaradaBrokering capabilities include, among other things, support for a wide variety of transport protocols, Java Message Service compliance, support for routing JXTA interactions, support for audio/video conferencing applications and, finally, support for multiple constraint specification formats such as XPath, SQL and regular expression queries. This paper demonstrates the suitability of NaradaBrokering to a wide variety of applications and scenarios. Copyright © 2005 John Wiley & Sons, Ltd.

Abstract We describe an information system architecture for the ACES (Asia–Pacific Cooperation for Earthquake Simulation) community. It addresses several key features of the field—simulations at multiple scales that need to be coupled together; real‐time and archival observational data, which needs to be analyzed for patterns and linked to the simulations; a variety of important algorithms including partial differential equation solvers, particle dynamics, signal processing and data analysis; a natural three‐dimensional space (plus time) setting for both visualization and observations; the linkage of field to real‐time events both as an aid to crisis management and to scientific discovery. We also address the need to support education and research for a field whose computational sophistication is rapidly increasing and spans a broad range. The information system assumes that all significant data is defined by an XML layer which could be virtual, but whose existence ensures that all data is object‐based and can be accessed and searched in this form. The various capabilities needed by ACES are defined as grid services, which are conformant with emerging standards and implemented with different levels of fidelity and performance appropriate to the application. Grid Services can be composed in a hierarchical fashion to address complex problems. The real‐time needs of the field are addressed by high‐performance implementation of data transfer and simulation services. Further, the environment is linked to real‐time collaboration to support interactions between scientists in geographically distant locations. Copyright © 2002 John Wiley & Sons, Ltd.