To leverage the power of crowdsourcing, we design and implement a Crowdsourcing Data Analytics System, CDAS. CDAS is a framework designed to support the deployment of various crowdsourcing applications. The core part of CDAS is a quality-sensitive answering model, which guides the crowdsourcing engine to process and monitor the human tasks. In this paper, we introduce the principles of our quality-sensitive model. To satisfy user required accuracy, the model guides the crowdsourcing query engine for the design and processing of the corresponding crowdsourcing jobs. It provides an estimated accuracy for each generated result based on the human workers' historical performances. When verifying the quality of the result, the model employs an online strategy to reduce waiting time. To show the effectiveness of the model, we implement and deploy two analytics jobs on CDAS, a twitter sentiment analytics job and an image tagging job. We use real Twitter and Flickr data as our queries respectively. We compare our approaches with state-of-the-art classification and image annotation techniques. The results show that the human-assisted methods can indeed achieve a much higher accuracy. By embedding the quality-sensitive model into crowdsourcing query engine, we effectively reduce the processing cost while maintaining the required query answer quality.}, acmid = {2336676}, author = {Liu, Xuan and Lu, Meiyu and Ooi, Beng Chin and Shen, Yanyan and Wu, Sai and Zhang, Meihui}, interhash = {41ad6e73b03373d76d3164ba248335d7}, intrahash = {2091967734f96c4afbc09319d48a8c65}, issn = {2150-8097}, issue_date = {June 2012}, journal = {Proceedings of the VLDB Endowment}, month = jun, number = 10, numpages = {12}, pages = {1040--1051}, publisher = {VLDB Endowment}, title = {CDAS: a crowdsourcing data analytics system}, url = {http://dl.acm.org/citation.cfm?id=2336664.2336676}, volume = 5, year = 2012 } @article{muniswamyreddy2010provenance, abstract = {Digital provenance is meta-data that describes the ancestry or history of a digital object. Most work on provenance focuses on how provenance increases the value of data to consumers. However, provenance is also valuable to storage providers. For example, provenance can provide hints on access patterns, detect anomalous behavior, and provide enhanced user search capabilities. As the next generation storage providers, cloud vendors are in the unique position to capitalize on this opportunity to incorporate provenance as a fundamental storage system primitive. To date, cloud offerings have not yet done so. We provide motivation for providers to treat provenance as first class data in the cloud and based on our experience with provenance in a local storage system, suggest a set of requirements that make provenance feasible and attractive.}, acmid = {1713258}, address = {New York, NY, USA}, author = {Muniswamy-Reddy, Kiran-Kumar and Seltzer, Margo}, doi = {10.1145/1713254.1713258}, interhash = {6fb5af3426b91f7e460d99746b3358c2}, intrahash = {1f9f6761cab2437739d30b9636ba5531}, issn = {0163-5980}, issue_date = {January 2010}, journal = {SIGOPS Operating Systems Review}, month = jan, number = 4, numpages = {6}, pages = {11--16}, publisher = {ACM}, title = {Provenance as first class cloud data}, url = {http://doi.acm.org/10.1145/1713254.1713258}, volume = 43, year = 2010 } @article{alsubaiee2012asterix, abstract = {At UC Irvine, we are building a next generation parallel database system, called ASTERIX, as our approach to addressing today's "Big Data" management challenges. ASTERIX aims to combine time-tested principles from parallel database systems with those of the Web-scale computing community, such as fault tolerance for long running jobs. In this demo, we present a whirlwind tour of ASTERIX, highlighting a few of its key features. We will demonstrate examples of our data definition language to model semi-structured data, and examples of interesting queries using our declarative query language. In particular, we will show the capabilities of ASTERIX for answering geo-spatial queries and fuzzy queries, as well as ASTERIX' data feed construct for continuously ingesting data.}, acmid = {2367532}, author = {Alsubaiee, Sattam and Altowim, Yasser and Altwaijry, Hotham and Behm, Alexander and Borkar, Vinayak and Bu, Yingyi and Carey, Michael and Grover, Raman and Heilbron, Zachary and Kim, Young-Seok and Li, Chen and Onose, Nicola and Pirzadeh, Pouria and Vernica, Rares and Wen, Jian}, interhash = {ae521b66302adb1b7df3f4cdb8d92181}, intrahash = {003f2654ae41861cfb77bf0353634ac3}, issn = {2150-8097}, issue_date = {August 2012}, journal = {Proceedings of the VLDB Endowment}, month = aug, number = 12, numpages = {4}, pages = {1898--1901}, publisher = {VLDB Endowment}, title = {ASTERIX: an open source system for "Big Data" management and analysis (demo)}, url = {http://dl.acm.org/citation.cfm?id=2367502.2367532}, volume = 5, year = 2012 } @article{behm2011asterix, abstract = {ASTERIX is a new data-intensive storage and computing platform project spanning UC Irvine, UC Riverside, and UC San Diego. In this paper we provide an overview of the ASTERIX project, starting with its main goal—the storage and analysis of data pertaining to evolving-world models . We describe the requirements and associated challenges, and explain how the project is addressing them. We provide a technical overview of ASTERIX, covering its architecture, its user model for data and queries, and its approach to scalable query processing and data management. ASTERIX utilizes a new scalable runtime computational platform called Hyracks that is also discussed at an overview level; we have recently made Hyracks available in open source for use by other interested parties. We also relate our work on ASTERIX to the current state of the art and describe the research challenges that we are currently tackling as well as those that lie ahead.}, address = {Netherlands}, affiliation = {University of California, Irvine, USA}, author = {Behm, Alexander and Borkar, Vinayak and Carey, Michael and Grover, Raman and Li, Chen and Onose, Nicola and Vernica, Rares and Deutsch, Alin and Papakonstantinou, Yannis and Tsotras, Vassilis}, doi = {10.1007/s10619-011-7082-y}, interhash = {3e06363406f716c5d9340dc2c693adb3}, intrahash = {42d96cc4877943527a9259424c584740}, issn = {0926-8782}, journal = {Distributed and Parallel Databases}, keyword = {Computer Science}, number = 3, pages = {185--216}, publisher = {Springer}, title = {ASTERIX: towards a scalable, semistructured data platform for evolving-world models}, url = {http://dx.doi.org/10.1007/s10619-011-7082-y}, volume = 29, year = 2011 } @inproceedings{abiteboul1998incremental, abstract = {Semistructured data is not strictly typed like relational or object-oriented data and may be irregular or incomplete. It often arises in practice, e.g., when heterogeneous data sources are integrated or data is taken from the World Wide Web. Views over semistructured data can be used to filter the data and to restructure (or provide structure to) it. To achieve fast query response time, these views are often materialized. This paper studies incremental maintenance techniques for materialized views over semistructured data. We use the graph-based data model OEM and the query language Lorel, developed at Stanford, as the framework for our work. We propose a new algorithm that produces a set of queries that compute the changes to the view based upon a change to the source. We develop an analytic cost model and compare the cost of executing our incremental maintenance algorithm to that of recomputing the view. We show that for nearly all types of database updates, it is more efficient to apply our incremental maintenance algorithm to the view than to recompute the view from the database, even when there are thousands of such updates.}, author = {Abiteboul, S. and McHugh, J. and Rys, M. and Vassalos, V. and Wiener, J.}, booktitle = {24rd International Conference on Very Large Data Bases}, interhash = {b395f09383de5eb21d34ad8c2b39ab59}, intrahash = {32903b757b4b4d118c77f4aeac4b0d94}, month = aug, pages = {38--49}, publisher = {Morgan Kaufmann}, title = {Incremental Maintenance for Materialized Views over Semistructured Data}, url = {http://ilpubs.stanford.edu:8090/340/}, year = 1998 } @article{dean2008mapreduce, abstract = {MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.}, acmid = {1327492}, address = {New York, NY, USA}, author = {Dean, Jeffrey and Ghemawat, Sanjay}, doi = {10.1145/1327452.1327492}, interhash = {b8a00982bf087c8543855897b7362a04}, intrahash = {bff539224836d703c2d21141985fa1a3}, issn = {0001-0782}, issue_date = {January 2008}, journal = {Communications of the ACM}, month = jan, number = 1, numpages = {7}, pages = {107--113}, publisher = {ACM}, title = {MapReduce: simplified data processing on large clusters}, url = {http://doi.acm.org/10.1145/1327452.1327492}, volume = 51, year = 2008 } @article{clauset2009powerlaw, abstract = {Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution—the part of the distribution representing large but rare events—and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov–Smirnov (KS) statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data, while in others the power law is ruled out.}, author = {Clauset, Aaron and Shalizi, Cosma Rohilla and Newman, M. E. J.}, doi = {10.1137/070710111}, interhash = {9ce8658af5a6358a758bfdb819f73394}, intrahash = {c0097d202655474b1db6811ddea03410}, issn = {0036-1445}, journal = {SIAM Review}, number = 4, pages = {661--703}, publisher = {SIAM}, title = {Power-Law Distributions in Empirical Data}, url = {http://link.aip.org/link/?SIR/51/661/1}, volume = 51, year = 2009 } @inproceedings{tatti2006dimension, abstract = {Many 0/1 datasets have a very large number of variables; however, they are sparse and the dependency structure of the variables is simpler than the number of variables would suggest. Defining the effective dimensionality of such a dataset is a nontrivial problem. We consider the problem of defining a robust measure of dimension for 0/1 datasets, and show that the basic idea of fractal dimension can be adapted for binary data. However, as such the fractal dimension is difficult to interpret. Hence we introduce the concept of normalized fractal dimension. For a dataset D, its normalized fractal dimension counts the number of independent columns needed to achieve the unnormalized fractal dimension of D. The normalized fractal dimension measures the degree of dependency structure of the data. We study the properties of the normalized fractal dimension and discuss its computation. We give empirical results on the normalized fractal dimension, comparing it against PCA.}, author = {Tatti, N. and Mielikainen, T. and Gionis, A. and Mannila, H.}, booktitle = {Proceedings of the Sixth IEEE International Conference on Data Mining (ICDM 2006)}, doi = {10.1109/ICDM.2006.167}, interhash = {5164cd6a09b802d14dce6d3947df60cd}, intrahash = {0a8ad03bc7d2d0d7d77ee73eede4ecc0}, issn = {1550-4786}, month = dec, organization = {IEEE}, pages = {603--612}, title = {What is the Dimension of Your Binary Data?}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4053086}, year = 2006 } @inproceedings{tramp2010weaving, abstract = {In this paper we tackle some of the most pressing obstacles of the emerging Linked Data Web, namely the quality, timeliness and coherence as well as direct end user benefits. We present an approach for complementing the Linked Data Web with a social dimension by extending the well-known Pingback mechanism, which is a technological cornerstone of the blogosphere, towards a Semantic Pingback. It is based on the advertising of an RPC service for propagating typed RDF links between Data Web resources. Semantic Pingback is downwards compatible with conventional Pingback implementations, thus allowing to connect and interlink resources on the Social Web with resources on the Data Web. We demonstrate its usefulness by showcasing use cases of the Semantic Pingback implementations in the semantic wiki OntoWiki and the Linked Data interface for database-backed Web applications Triplify. }, address = {Berlin / Heidelberg}, author = {Tramp, Sebastian and Frischmuth, Philipp and Ermilov, Timofey and Auer, Sören}, booktitle = {Proceedings of the EKAW 2010 - Knowledge Engineering and Knowledge Management by the Masses; 11th October-15th October 2010 - Lisbon, Portugal}, editor = {Cimiano, P. and Pinto, H.S.}, interhash = {c2e23e5a78627b560bb3103ecd52a410}, intrahash = {0e32b9750ee77fcdf2162f70aaee5622}, month = oct, pages = {135--149}, publisher = {Springer}, series = {Lecture Notes in Artificial Intelligence}, timestamp = {2010.06.16}, title = {Weaving a Social Data Web with Semantic Pingback}, url = {http://svn.aksw.org/papers/2010/EKAW_SemanticPingback/public.pdf}, volume = 6317, year = 2010 } @book{tufte2001visual, asin = {0961392142}, author = {Tufte, Edward R.}, dewey = {001.4226}, ean = {9780961392147}, edition = {Second}, interhash = {9900880e451150c1b06ede3c780c062b}, intrahash = {9c028ebcb336380cb02e2a4beaa14d54}, isbn = {0961392142}, publisher = {Graphics Press}, title = {The Visual Display of Quantitative Information}, url = {http://www.amazon.com/Visual-Display-Quantitative-Information-2nd/dp/0961392142%3FSubscriptionId%3D192BW6DQ43CK9FN0ZGG2%26tag%3Dws%26linkCode%3Dxm2%26camp%3D2025%26creative%3D165953%26creativeASIN%3D0961392142}, year = 2001 } @incollection{fayyad1996data, abstract = {Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future research directions in the field.}, address = {Menlo Park, CA, USA}, author = {Fayyad, Usama M. and Piatetsky-Shapiro, Gregory and Smyth, Padhraic}, booktitle = {Advances in knowledge discovery and data mining}, editor = {Fayyad, Usama M. and Piatetsky-Shapiro, Gregory and Smyth, Padhraic and Uthurusamy, Ramasamy}, interhash = {79663e4b1f464b82ce1ae45345dc424f}, intrahash = {3f5a400d01a974f993cee1ac5f79cfc8}, isbn = {0-262-56097-6}, pages = {1--34}, publisher = {American Association for Artificial Intelligence}, title = {From data mining to knowledge discovery: an overview}, url = {http://portal.acm.org/citation.cfm?id=257942}, year = 1996 } @article{331504, abstract = {Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.}, address = {New York, NY, USA}, author = {Jain, A. K. and Murty, M. N. and Flynn, P. J.}, doi = {http://doi.acm.org/10.1145/331499.331504}, interhash = {5113b61d428d4de4423182e5f2b2f468}, intrahash = {b19bcef82a04eb82ee4abde53ee7d1c2}, issn = {0360-0300}, journal = {ACM Comput. Surv.}, number = 3, pages = {264--323}, publisher = {ACM}, title = {Data clustering: a review}, url = {http://portal.acm.org/citation.cfm?id=331499.331504&coll=Portal&dl=ACM&CFID=26215063&CFTOKEN=18848029}, volume = 31, year = 1999 } @article{romero07, abstract = {Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. This paper surveys the application of data mining to traditional educational systems, particular web-based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems. Each of these systems has different data source and objectives for knowledge discovering. After preprocessing the available data in each case, data mining techniques can be applied: statistics and visualization; clustering, classification and outlier detection; association rule mining and pattern mining; and text mining. The success of the plentiful work needs much more specialized work in order for educational data mining to become a mature area.}, address = {Tarrytown, NY, USA}, author = {Romero, C. and Ventura, S.}, doi = {http://dx.doi.org/10.1016/j.eswa.2006.04.005}, interhash = {89d843f1a3b181f2a628e881d9210b22}, intrahash = {746d12e92e58587461ffcb8dc381e283}, issn = {0957-4174}, journal = {Expert Syst. Appl.}, number = 1, pages = {135--146}, publisher = {Pergamon Press, Inc.}, title = {Educational data mining: A survey from 1995 to 2005}, url = {http://portal.acm.org/citation.cfm?id=1223659}, volume = 33, year = 2007 } @article{wu2008wu, abstract = {This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community.With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current andfurther research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, associationanalysis, and link mining, which are all among the most important topics in data mining research and development.}, address = {London}, author = {Wu, Xindong and Kumar, Vipin and Quinlan, J. Ross and Ghosh, Joydeep and Yang, Qiang and Motoda, Hiroshi and McLachlan, Geoffrey and Ng, Angus and Liu, Bing and Yu, Philip and Zhou, Zhi-Hua and Steinbach, Michael and Hand, David and Steinberg, Dan}, interhash = {76fd294a34cf85638f6e194a85af8db9}, intrahash = {2c34bb4b49187a6d3e780e78d254ae1f}, issn = {0219-1377}, journal = {Knowledge and Information Systems}, month = Jan, number = 1, pages = {1--37}, publisher = {Springer}, title = {Top 10 algorithms in data mining}, url = {http://dx.doi.org/10.1007/s10115-007-0114-2}, volume = 14, year = 2008 } @inproceedings{conf/sdm/AggarwalY05, author = {Aggarwal, Charu C. and Yu, Philip S.}, booktitle = {SDM}, interhash = {e1487d660a1614b50bd756f7383b98ea}, intrahash = {bb72c8baa786e98565c4a7448ecae59a}, title = {Online Analysis of Community Evolution in Data Streams.}, url = {http://web.mit.edu/charu/www/aggar142.pdf }, year = 2005 }