50,996 research outputs found
Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments
Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
User response prediction, which models the user preference w.r.t. the
presented items, plays a key role in online services. With two-decade rapid
development, nowadays the cumulated user behavior sequences on mature Internet
service platforms have become extremely long since the user's first
registration. Each user not only has intrinsic tastes, but also keeps changing
her personal interests during lifetime. Hence, it is challenging to handle such
lifelong sequential modeling for each individual user. Existing methodologies
for sequential modeling are only capable of dealing with relatively recent user
behaviors, which leaves huge space for modeling long-term especially lifelong
sequential patterns to facilitate user modeling. Moreover, one user's behavior
may be accounted for various previous behaviors within her whole online
activity history, i.e., long-term dependency with multi-scale sequential
patterns. In order to tackle these challenges, in this paper, we propose a
Hierarchical Periodic Memory Network for lifelong sequential modeling with
personalized memorization of sequential patterns for each user. The model also
adopts a hierarchical and periodical updating mechanism to capture multi-scale
sequential patterns of user interests while supporting the evolving user
behavior logs. The experimental results over three large-scale real-world
datasets have demonstrated the advantages of our proposed model with
significant improvement in user response prediction performance against the
state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets:
https://github.com/alimamarankgroup/HPM
Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization
Protecting vast quantities of data poses a daunting challenge for the growing
number of organizations that collect, stockpile, and monetize it. The ability
to distinguish data that is actually needed from data collected "just in case"
would help these organizations to limit the latter's exposure to attack. A
natural approach might be to monitor data use and retain only the working-set
of in-use data in accessible storage; unused data can be evicted to a highly
protected store. However, many of today's big data applications rely on machine
learning (ML) workloads that are periodically retrained by accessing, and thus
exposing to attack, the entire data store. Training set minimization methods,
such as count featurization, are often used to limit the data needed to train
ML workloads to improve performance or scalability. We present Pyramid, a
limited-exposure data management system that builds upon count featurization to
enhance data protection. As such, Pyramid uniquely introduces both the idea and
proof-of-concept for leveraging training set minimization methods to instill
rigor and selectivity into big data management. We integrated Pyramid into
Spark Velox, a framework for ML-based targeting and personalization. We
evaluate it on three applications and show that Pyramid approaches
state-of-the-art models while training on less than 1% of the raw data
Modeling Users Feedback Using Bayesian Methods for Data-Driven Requirements Engineering
Data-driven requirements engineering represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. App developers now receive abundant user feedback from user comments in app stores and social media, i.e., explicit feedback, to feedback from usage data and system logs, i.e, implicit feedback. In this dissertation, we describe two novel Bayesian approaches that utilize the available user\u27s to support requirements decisions and activities in the context of applications delivered through software marketplaces (web and mobile). In the first part, we propose to exploit implicit user feedback in the form of usage data to support requirements prioritization and validation. We formulate the problem as a popularity prediction problem and present a novel Bayesian model that is highly interpretable and offers early-on insights that can be used to support requirements decisions. Experimental results demonstrate that the proposed approach achieves high prediction accuracy and outperforms competitive models. In the second part, we discuss the limitations of previous approaches that use explicit user feedback for requirements extraction, and alternatively, propose a novel Bayesian approach that can address those limitations and offer a more efficient and maintainable framework. The proposed approach (1) simplifies the pipeline by accomplishing the classification and summarization tasks using a single model, (2) replaces manual steps in the pipeline with unsupervised alternatives that can accomplish the same task, and (3) offers an alternative way to extract requirements using example-based summaries that retains context. Experimental results demonstrate that the proposed approach achieves equal or better classification accuracy and outperforms competitive models in terms of summarization accuracy. Specifically, we show that the proposed approach can capture 91.3% of the discussed requirement with only 19% of the dataset, i.e., reducing the human effort needed to extract the requirements by 80%
Harnessing the power of the general public for crowdsourced business intelligence: a survey
International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI
Modeling the scaling properties of human mobility
While the fat tailed jump size and the waiting time distributions
characterizing individual human trajectories strongly suggest the relevance of
the continuous time random walk (CTRW) models of human mobility, no one
seriously believes that human traces are truly random. Given the importance of
human mobility, from epidemic modeling to traffic prediction and urban
planning, we need quantitative models that can account for the statistical
characteristics of individual human trajectories. Here we use empirical data on
human mobility, captured by mobile phone traces, to show that the predictions
of the CTRW models are in systematic conflict with the empirical results. We
introduce two principles that govern human trajectories, allowing us to build a
statistically self-consistent microscopic model for individual human mobility.
The model not only accounts for the empirically observed scaling laws but also
allows us to analytically predict most of the pertinent scaling exponents
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