Indexing Metric Uncertain Data For Range Queries And Range Joins,
2017
Zhejiang University
Indexing Metric Uncertain Data For Range Queries And Range Joins, Lu Chen, Yunjun Gao, Aoxiao Zhong, Christian S. Jensen, Gang Chen, Baihua Zheng
Research Collection School Of Computing and Information Systems
Range queries and range joins in metric spaces have applications in many areas, including GIS, computational biology, and data integration, where metric uncertain data exist in different forms, resulting from circumstances such as equipment limitations, high-throughput sequencing technologies, and privacy preservation. We represent metric uncertain data by using an object-level model and a bi-level model, respectively. Two novel indexes, the uncertain pivot B+-tree (UPB-tree) and the uncertain pivot B+-forest (UPB-forest), are proposed in order to support probabilistic range queries and range joins for a wide range of uncertain data types and similarity metrics. Both index structures use a small set …
Time-Aware Conversion Prediction,
2017
East China Normal University
Time-Aware Conversion Prediction, Wendi Ji, Xiaoling Wang, Feida Zhu
Research Collection School Of Computing and Information Systems
The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that different products take different time periods for conversion. The “conversion” here refers to actually a more general set of pre-defined actions, including for example purchases or registrations in recommendation and advertising systems. The mismatch between the product’s actual conversion period and the application’s target conversion period has been the subtle culprit compromising many existing recommendation algorithms.The challenging question: what products should be recommended for a given time period to maximize …
Geometric Approaches For Top-K Queries [Tutorial],
2017
Singapore Management University
Geometric Approaches For Top-K Queries [Tutorial], Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
Top-k processing is a well-studied problem with numerous applications that is becoming increasingly relevant with the growing availability of recommendation systems and decision-making software. The objective of this tutorial is twofold. First, we will delve into the geometric aspects of top-k processing. Second, we will cover complementary features to top-k queries, with strong practical relevance and important applications, that have a computational geometric nature. The tutorial will close with insights in the effect of dimensionality on the meaningfulness of top-k queries, and interesting similarities to nearest neighbor search.
Smartphone Sensing Meets Transport Data: A Collaborative Framework For Transportation Service Analytics,
2017
Beijing Normal University
Smartphone Sensing Meets Transport Data: A Collaborative Framework For Transportation Service Analytics, Yu Lu, Archan Misra, Wen Sun, Huayu Wu
Research Collection School Of Computing and Information Systems
We advocate for and introduce TRANSense, a framework for urban transportation service analytics that combines participatory smartphone sensing data with city-scale transportation-related transactional data (taxis, trains etc.). Our work is driven by the observed limitations of using each data type in isolation: (a) commonly-used anonymous city-scale datasets (such as taxi bookings and GPS trajectories) provide insights into the aggregate behavior of transport infrastructure, but fail to reveal individual-specific transport experiences (e.g., wait times in taxi queues); while (b) mobile sensing data can capture individual-specific commuting-related activities, but suffers from accuracy and energy overhead challenges due to usage artefacts and lack …
Learning Homophily Couplings From Non-Iid Data For Joint Feature Selection And Noise-Resilient Outlier Detection,
2017
Singapore Management University
Learning Homophily Couplings From Non-Iid Data For Joint Feature Selection And Noise-Resilient Outlier Detection, Guansong Pang, Longbing Cao, Ling Chen, Huan Liu
Research Collection School Of Computing and Information Systems
This paper introduces a novel wrapper-based outlier detection framework (WrapperOD) and its instance (HOUR) for identifying outliers in noisy data (i.e., data with noisy features) with strong couplings between outlying behaviors. Existing subspace or feature selection-based methods are significantly challenged by such data, as their search of feature subset(s) is independent of outlier scoring and thus can be misled by noisy features. In contrast, HOUR takes a wrapper approach to iteratively optimize the feature subset selection and outlier scoring using a top-k outlier ranking evaluation measure as its objective function. HOUR learns homophily couplings between outlying behaviors (i.e., abnormal behaviors …
Discovering Newsworthy Themes From Sequenced Data: A Step Towards Computational Journalism,
2017
Singapore Management University
Discovering Newsworthy Themes From Sequenced Data: A Step Towards Computational Journalism, Qi Fan, Yuchen Li, Dongxiang Zhang, Kian-Lee Tan Tan
Research Collection School Of Computing and Information Systems
Automatic discovery of newsworthy themes from sequenced data can relieve journalists from manually poring over a large amount of data in order to find interesting news. In this paper, we propose a novel k -Sketch query that aims to find k striking streaks to best summarize a subject. Our scoring function takes into account streak strikingness and streak coverage at the same time. We study the k -Sketch query processing in both offline and online scenarios, and propose various streak-level pruning techniques to find striking candidates. Among those candidates, we then develop approximate methods to discover the k most representative …
Attribute-Based Encryption With Expressive And Authorized Keyword Search,
2017
Singapore Management University
Attribute-Based Encryption With Expressive And Authorized Keyword Search, Hui Cui, Robert H. Deng, Joseph K. Liu, Yingjiu Li
Research Collection School Of Computing and Information Systems
To protect data security and privacy in cloud storage systems, a common solution is to outsource data in encrypted forms so that the data will remain secure and private even if storage systems are compromised. The encrypted data, however, must be pliable to search and access control. In this paper, we introduce a notion of attribute-based encryption with expressive and authorized keyword search (ABE-EAKS) to support both expressive keyword search and fine-grained access control over encrypted data in the cloud. In ABE-EAKS, every data user is associated with a set of attributes and is issued a private attribute-key corresponding to …
Mining Capstone Project Wikis For Knowledge Discovery,
2017
Singapore Management University
Mining Capstone Project Wikis For Knowledge Discovery, Swapna Gottipati, Venky Shankararaman, Melvrivk Goh
Research Collection School Of Computing and Information Systems
Wikis are widely used collaborative environments as sources of information and knowledge. The facilitate students to engage in collaboration and share information among members and enable collaborative learning. In particular, Wikis play an important role in capstone projects. Wikis aid in various project related tasks and aid to organize information and share. Mining project Wikis is critical to understand the students learning and latest trends in industry. Mining Wikis is useful to educationists and academicians for decision-making about how to modify the educational environment to improve student's learning. The main challenge is that the content or data in project Wikis …
One-To-Cloud One-Time Pad Data Encryption: Introducing Virtual Prototyping With Pspice,
2017
Technological University Dublin
One-To-Cloud One-Time Pad Data Encryption: Introducing Virtual Prototyping With Pspice, Paul Tobin, Lee Tobin, Roberto Gandia Blanquer Dr, Michael Mckeever, Jonathan Blackledge
Conference papers
In this paper, we examine the design and application of a one-time pad encryption system for protecting data stored in the Cloud. Personalising security using a one-time pad generator at the client-end protects data from break-ins, side-channel attacks and backdoors in public encryption algorithms. The one-time pad binary sequences were obtained from modified analogue chaos oscillators initiated by noise and encoded client data locally. Specific ``one-to-Cloud'' storage applications returned control back to the end user but without the key distribution problem normally associated with one-time pad encryption. Development of the prototype was aided by ``Virtual Prototyping'' in the latest version …
Blend It Applications And Server,
2017
California Polytechnic State University, San Luis Obispo
Blend It Applications And Server, Rebecca Mckinley, Cory Mayer, Tyler Fox, Alex Bartlett, Aly Chapman
Computer Engineering
No abstract provided.
Smartgarden,
2017
California Polytechnic State University, San Luis Obispo
Smartgarden, Samuel S. Lees, Matthew T. Lindly
Computer Engineering
The problem that the SmartGarden project seeks to solve is the challenge of watering succulents correctly and maintaining consistent plant health. Too much water can cause root rot and drowning while too little water can cause dehydration and starvation.
The SmartGarden solves this common problem by configuring a Raspberry Pi to measure soil moisture, store the data to an AWS server in the cloud, and water the succulent when necessary, informing the user by providing both a web-interface and a variety of notifications.
Table Of Contents Jitim Vol 26 Issue 2, 2017,
2017
California State University, San Bernardino
Table Of Contents Jitim Vol 26 Issue 2, 2017
Journal of International Technology and Information Management
Table of Contents JITIM vol 26 issue 2, 2017
Cpu Db Data Visualization,
2017
California Polytechnic State University, San Luis Obispo
Cpu Db Data Visualization, Ruchita Patel, Marek Moreno
Computer Engineering
Given the CPU database from Stanford, we wanted to create something that portrayed the data in a more visually pleasing way. The CPU database website wanted a web page that would allow users to create graphs based on the processor data from the database. The web page would allow users to select different data from the database and create the graphs they wanted to gain insight into the decades of processor data.
Comparative Analysis Of Big Data Analytics Software In Assessing Sample Data,
2017
UOWD
Comparative Analysis Of Big Data Analytics Software In Assessing Sample Data, Soly Mathew Biju, Alex Mathew
Journal of International Technology and Information Management
Over the last few years, big data has emerged as an important topic of discussion in most firms owing to its ability of creation, storage and processing of content at a reasonable price. Big data consists of advanced tools and techniques to process large volumes of data in organisations. Investment in big data analytics has almost become a necessity in large-sized firms, particularly multinational companies, for its unique benefits, particularly in prediction and identification of various trends. Some of the most popular big data analytics software used today are MapReduce, Hive, Tableau and Hive, while the framework Hadoop enables easy …
Is The Whole Greater Than The Sum Of Its Parts?,
2017
Singapore Management University
Is The Whole Greater Than The Sum Of Its Parts?, Liangyue Li, Hanghang Tong, Yong Wang, Conglei Shi, Nan Cao, Norbou Buchler
Research Collection School Of Computing and Information Systems
The PART-WHOLE relationship routinely finds itself in many disciplines, ranging from collaborative teams, crowdsourcing, autonomous systems to networked systems. From the algorithmic perspective, the existing work has primarily focused on predicting the outcomes of the whole and parts, by either separate models or linear joint models, which assume the outcome of the parts has a linear and independent effect on the outcome of the whole. In this paper, we propose a joint predictive method named PAROLE to simultaneously and mutually predict the part and whole outcomes. The proposed method offers two distinct advantages over the existing work. First (Model Generality), …
Sap: Improving Continuous Top-K Queries Over Streaming Data,
2017
Northeastern University
Sap: Improving Continuous Top-K Queries Over Streaming Data, Rui Zhu, Bin Wang, Xiaochun Yang, Baihua Zheng, Guoren Wang
Research Collection School Of Computing and Information Systems
Continuous top-k query over streaming data is a fundamental problem in database. In this paper, we focus on the sliding window scenario, where a continuous top-k query returns the top-k objects within each query window on the data stream. Existing algorithms support this type of queries via incrementally maintaining a subset of objects in the window and try to retrieve the answer from this subset as much as possible whenever the window slides. However, since all the existing algorithms are sensitive to query parameters and data distribution, they all suffer from expensive incremental maintenance cost. In this paper, we propose …
Understanding Deleted File Decay On Removable Media Using Differential Analysis,
2017
George Mason University
Understanding Deleted File Decay On Removable Media Using Differential Analysis, James H. Jones Jr, Anurag Srivastava, Josh Mosier, Connor Anderson, Seth Buenafe
Annual ADFSL Conference on Digital Forensics, Security and Law
Digital content created by picture recording devices is often stored internally on the source device, on either embedded or removable media. Such storage media is typically limited in capacity and meant primarily for interim storage of the most recent image files, and these devices are frequently configured to delete older files as necessary to make room for new files. When investigations involve such devices and media, it is sometimes these older deleted files that would be of interest. It is an established fact that deleted file content may persist in part or in its entirety after deletion, and identifying the …
Developing And Implementing An Online Research Data Repository For Your University Or College Campus,
2017
Texas State University
Developing And Implementing An Online Research Data Repository For Your University Or College Campus, Raymond J. Uzwyshyn
Digital Initiatives Symposium
Data-driven research is becoming increasingly important on university and college campuses. Most US federal and many international granting agencies mandatorily require that researchers applying for public grants possess a data management plan and make their research and data publically available through online access. This presentation overviews online research data repositories and implementation strategies for university and college campus libraries. The presentation pragmatically surveys this newer technology landscape and how organizations can begin to think about and implement an online data research repository. This session will survey the landscape but also makes use of practical example from Texas State University and …
Cryptography And Data Security In Cloud Computing,
2017
Xidian University
Cryptography And Data Security In Cloud Computing, Zheng Yan, Robert H. Deng, Vijay Varadharajan
Research Collection School Of Computing and Information Systems
Cloud computing offers a new way of services by re-arranging various resources and providing them to users based on their demands. It also plays an important role in the next generation mobile networks and services (5G) and Cyber-Physical and Social Computing (CPSC). Storing data in the cloud greatly reduces storage burden of users and brings them access convenience, thus it has become one of the most important cloud services. However, cloud data security, privacy and trust become a crucial issue that impacts the success of cloud computing and may impede the development of 5G and CPSC. First, storing data at …
A Data-Driven Approach For Benchmarking Energy Efficiency Of Warehouse Buildings,
2017
Singapore Management University
A Data-Driven Approach For Benchmarking Energy Efficiency Of Warehouse Buildings, Wee Leong Lee, Kar Way Tan, Zui Young Lim
Research Collection School Of Computing and Information Systems
This study proposes adata-driven approach for benchmarking energy efficiency of warehouse buildings.Our proposed approach provides an alternative to the limitation of existingbenchmarking approaches where a theoretical energy-efficient warehouse was usedas a reference. Our approach starts by defining the questions needed to capturethe characteristics of warehouses relating to energy consumption. Using an existingdata set of warehouse building containing various attributes, we first cluster theminto groups by their characteristics. The warehouses characteristics derivedfrom the cluster assignments along with their past annual energy consumptionare subsequently used to train a decision tree model. The decision tree providesa classification of what factors contribute to different …
