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Companies providing cloud-scale data services have increasing needs to store and analyze massive data sets (e.g., search logs, click streams, and web graph data). For cost and performance reasons, processing is typically done on large clusters of thousands of commodity machines by using high level scripting languages. In the recent past, there has been significant progress in adapting well-known techniques from traditional relational DBMSs to this new scenario. However, important challenges remain open. In this paper we study the very common join operation, discuss some unique challenges in the large-scale distributed scenario, and explain how to efficiently and robustly process joins in a distributed way. Specifically, we introduce novel execution strategies that leverage opportunities not available in centralized scenarios, and others that robustly handle data skew. We report experimental validations of our approaches on Scope production clusters, which power the Applications and Services Group at Microsoft.

In this paper, we present a purpose-built data management system, MLdp, for all machine learning (ML) datasets. ML applications pose some unique requirements different from common conventional data processing applications, including but not limited to: data lineage and provenance tracking, rich data semantics and formats, integration with diverse ML frameworks and access patterns, trial-and-error driven data exploration and evolution, rapid experimentation, reproducibility of the model training, strict compliance and privacy regulations, etc. Current ML systems/services, often named MLaaS, to-date focus on the ML algorithms, and offer no integrated data management system. Instead, they require users to bring their own data and to manage their own data on either blob storage or on file systems. The burdens of data management tasks, such as versioning and access control, fall onto the users, and not all compliance features, such as terms of use, privacy measures, and auditing, are available. MLdp offers a minimalist and flexible data model for all varieties of data, strong version management to guarantee re-producibility of ML experiments, and integration with major ML frameworks. MLdp also maintains the data provenance to help users track lineage and dependencies among data versions and models in their ML pipelines. In addition to table-stake features, such as security, availability and scalability, MLdp's internal design choices are strongly influenced by the goal to support rapid ML experiment iterations, which

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Purpose A product communicates to consumers through its form and function, which may generate an effective response. Little is known, however, about the impact of the interaction of form and functional newness on consumers’ adoption preference. Drawing on uniqueness theory, this research aims to propose that the relative importance of form and functional newness to adoption preference could vary depending on the degree of consumers’ need for uniqueness (CNFU). Design/methodology/approach To mimic real consumption behavior as much as possible in these studies, the authors first choose a product that the respondents are familiar with and use on a daily basis. Second, the authors conduct a series of conjoint analysis in which respondents are presented with a set of options simultaneously and are asked to make a choice of adoption among those options. The authors conduct three conjoint studies using students and adult consumers. Findings Evidence from three conjoint studies using both student and adult consumer samples confirms the moderating role of CNFU. The results indicate that form and functional newness positively impact adoption preference, the positive effect of form newness is weakened in a compare-and-choose decision when functional newness is in place and this weakened interaction effect is mitigated with increasing CNFU. Research limitations/implications This research makes several contributions to the extant literature. First, the authors investigate the moderating role of CNFU in the interplay between form and functional newness. By identifying a distinctive pattern between high- vs low-CNFU consumers, the authors propose a new aspect to explain the inconclusive results of the interaction effects in previous studies. Extending this line of research, the authors show that there is a dynamic component to the positive influence of form and functional newness on adoption preference. Consumers’ preference for form newness, relative to functional newness, is likely to be lessened with the decrease in their need for uniqueness. Second, this research goes beyond the survey or sales data approaches of prior studies to examine the interaction of form and function in a context that reflects actual decision processes. Assuming that consumers have access to a set of options before making an adoption decision, the authors are able to determine their priorities and preferences for new products. Using conjoint analysis, the authors observe consumers make a trade-off between form and functional newness. This approach allows us to investigate the relative importance of form and functional newness in affecting consumers’ adoption decision. Finally, the consistency of the results of these three studies enhances the robustness of this research. Practical implications While consumers appreciate improved and newer functionality in general, this may not be the case for a novel form. For consumers who desire to belong or to fit into social norms, adopting a product with an extreme atypical form could be risky and provoke a negative social response. For those with such conservative attitudes, learning costs are likely to overshadow the excitement of owning a radical product. Thus, a product with high functional newness and standard form would be the right choice for this group of consumers. On the other hand, consumers with high CNFU are more likely to overcome concerns regarding the risks and learning costs of a novel form due to their desire to use the unconventional product display to differentiate themselves and establish their uniqueness. Therefore, a product with high functional newness and novel form may be more favorable for them. With this insight, marketers can better define their market segment and position their product strengths. For example, in the competitive smart phone industry, some brands may try to focus on high form newness to capture high-CNFU consumers (e.g. LG Flex curved cell phone). Originality/value First, the authors propose the moderating role of CNFU to explain the gap in the literature. This new view provides product managers and marketers with a better understanding of how consumers in different consumer segments (e.g. high vs low degree of CNFU) behave distinctively in their response to form and functional newness. Second, most of the literature on consumer response to product form has focused on consumer opinion, attitude, perception or product evaluation. This study focuses on measuring consumers’ adoption preference through a conjoint approach. This distinction is important because a positive attitude does not necessarily translate to adoption when consumers make their final choice decision. Third, prior studies test the effects of form and function using sales data or between subject experiments where respondents only view a single product. This approach is less representative of real adoption behavior when the reality is consumers often compare a set of options simultaneously and make an adoption decision among a pool of available options.

Centrality is widely recognized as one of the most critical measures to provide insight in the structure and function of complex networks. While various centrality measures have been proposed for single-layer networks, a general framework for studying centrality in multilayer networks (i.e., multicentrality) is still lacking. In this study, a tensor-based framework is introduced to study eigenvector multicentrality, which enables the quantification of the impact of interlayer influence on multicentrality, providing a systematic way to describe how multicentrality propagates across different layers. This framework can leverage prior knowledge about the interplay among layers to better characterize multicentrality for varying scenarios. Two interesting cases are presented to illustrate how to model multilayer influence by choosing appropriate functions of interlayer influence and design algorithms to calculate eigenvector multicentrality. This framework is applied to analyze several empirical multilayer networks, and the results corroborate that it can quantify the influence among layers and multicentrality of nodes effectively.

Information-theoretically provable unique true random numbers, which cannot be correlated or controlled by an attacker, can be generated based on quantum measurement of vacuum state and universal-hashing randomness extraction. Quantum entropy in the measurements decides the quality and security of the random number generator. At the same time, it directly determine the extraction ratio of true randomness from the raw data, in other words, it affects quantum random numbers generating rate obviously. In this work, considering the effects of classical noise, the best way to enhance quantum entropy in the vacuum-based quantum random number generator is explored in the optimum dynamical analog-digital converter (ADC) range scenario. The influence of classical noise excursion, which may be intrinsic to a system or deliberately induced by an eavesdropper, on the quantum entropy is derived. We propose enhancing local oscillator intensity rather than electrical gain for noise-independent amplification of quadrature fluctuation of vacuum state. Abundant quantum entropy is extractable from the raw data even when classical noise excursion is large. Experimentally, an extraction ratio of true randomness of 85.3% is achieved by finite enhancement of the local oscillator power when classical noise excursions of the raw data is obvious.Keywords: quantum random number; vacuum state; maximization of quantum conditional minentropy;

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