PubMed Google Scholar. Discriminative analysis in Big Data Analytics can be the primary purpose of the data analysis, or it can be performed to conduct tagging (such as semantic tagging) on the data for the purpose of searching. An important advantage of more abstract representations is that they can be invariant to the local changes in the input data. Various organizations have invested in developing products using Big Data Analytics to addressing their monitoring, experimentation, data analysis, simulations, and other knowledge and business needs , making it a central topic in data science research. J Mach Learn Res 2009, 10: 1–40. pp 127–135. All authors read and approved the final manuscript. In their experiments they obtained neurons that function like face detectors, cat detectors, and human body detectors, and based on these features their approach also outperformed the state-of-the-art and recognized 22,000 object categories from the ImageNet dataset. Papers on statistics, biostatistics, econometrics, computational methodologies, and … In some Big Data domains, the input corpus consists of a mix of both labeled and unlabeled data, e.g., cyber security , fraud detection , and computer vision . Variety in Big Data, and may minimize need for input from human experts to extract features from every new data type observed in Big Data. Submit an article Journal homepage. Privacy In particular, more work is necessary on how we can adapt Deep Learning algorithms for problems associated with Big Data, including high dimensionality, streaming data analysis, scalability of Deep Learning models, improved formulation of data abstractions, distributed computing, semantic indexing, data tagging, information retrieval, criteria for extracting good data representations, and domain adaptation. Big data analytics is defined as the processing of vast amount of data using mathematics and statistical modeling, programming and computing … Deep learning algorithms use a huge amount of unsupervised data to automatically extract complex representation. Domain adaptation during learning is an important focus of study in Deep Learning ,, where the distribution of the training data (from which the representations are learnt) is different from the distribution of the test data (on which the learnt representations are deployed). In Strata 2012: Making Data Work. Deep Learning solutions have yielded outstanding results in different machine learning applications, including speech recognition -, computer vision ,,, and natural language processing -. In: Proceeding of the 29th International Conference in Machine Learning, Edingburgh, Scotland. Most of the presented approaches in data mining are not usually able to handle the large datasets successfully. Document (or textual) representation is a key aspect in information retrieval for many domains. 2. pp 1150–1157, Bengio Y, LeCun Y: Scaling learning algorithms towards, AI.In Large Scale Kernel Machines Edited by: Bottou L, Chapelle O, DeCoste D, Weston J. MIT Press, Cambridge, MA; 2007, 321–360. Incrementally, the samples that do not conform to the given objective function (for example, their classification error is more than a threshold, or their reconstruction error is high) are collected and are used for adding new nodes to the hidden layer, with these new nodes being initialized based on those samples. The primary idea is to train multiple versions of the model in parallel, each running on a different node in the network and analyzing different subsets of data. The objective is to learn a complicated and abstract representation of the data in a hierarchical manner by passing the data through multiple transformation layers. Previous works used to adapt hand designed feature for images like SIFT and HOG to the video domain. RBMs are most likely the most popular version of Boltzmann machine . Part of The authors declare that they have no competing interests. Hinton et al. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. In the present study, we explore how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. If the hidden layer is linear and the mean squared error is used as the reconstruction criteria, then the Autoencoder will learn the first k principle components of the data. Moreover, marginalized SDA only has two free meta-parameters, controlling the amount of noise as well as the number of layers to be stacked, which greatly simplifies the model selection process. Distributed data mining was introduced to speed up the data mining process to handle big data sets and to be able to work in environments with inherent data distribution. For example, the Histogram of Oriented Gradients (HOG)  and Scale Invariant Feature Transform (SIFT)  are popular feature engineering algorithms developed specifically for the computer vision domain. Big Data generally refers to data that exceeds the typical storage, processing, and computing capacity of conventional databases and data analysis techniques. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11). Other useful characteristics of the learnt abstract representations by Deep Learning include: (1) relatively simple linear models can work effectively with the knowledge obtained from the more complex and more abstract data representations, (2) increased automation of data representation extraction from unsupervised data enables its broad application to different data types, such as image, textural, audio, etc., and (3) relational and semantic knowledge can be obtained at the higher levels of abstraction and representation of the raw data. In: International Conference on Artificial Intelligence and Statistics. Models based on shallow learning architectures such as decision trees, support vector machines, and case-based reasoning may fall short when attempting to extract useful information from complex structures and relationships in the input corpus. Techniques such as semantic hashing are quite attractive for information retrieval, because documents that are similar to the query document can be retrieved by finding all the memory addresses that differ from the memory address of the query document by a few bits. IEEE International Conference on Big Data: 18: 30: Conference: 17: Advances in Data Analysis and Classification: 18: 25: Journal: 18: Statistical Analysis and Data Mining: 17: 30: Journal: 19: BioData Mining: 17: 25: Journal : 20: Intelligent Data Analysis: 16: 21: Journal . More traditional machine learning and feature engineering algorithms are not efficient enough to extract the complex and non-linear patterns generally observed in Big Data. Moreover, the question of defining the criteria required for extracting good data representations leads to the question of what would constitute a good data representation that is effective for semantic indexing and/or data tagging. Invited Keynote Speaker. A computational cluster of 1000 machines and 16000 cores was used to train the network with model parallelism and asynchronous SGD (Stochastic Gradient Descent). JMLR.org. Another key area of interest would be to explore the question of what criteria is necessary and should be defined for allowing the extracted data representations to provide useful semantic meaning to the Big Data. Salton G, Buckley C: Term-weighting approaches in automatic text retrieval.  describe a Deep Learning generative model to learn the binary codes for documents. Working with the Variety among different data representations in a given repository poses unique challenges with Big Data, which requires Big Data preprocessing of unstructured data in order to extract structured/ordered representations of the data for human and/or downstream consumption. 10.1109/MCI.2010.938364, Hinton GE, Osindero S, Teh Y-W: A fast learning algorithm for deep belief nets. Case Studies In Business, Industry And Government Statistics, electronic journal, Bentley University. The binary code of the documents can then be used for information retrieval. Tags. Dumbill E: What Is Big Data? Chopra et al. Binary codes require relatively little storage space, and in addition they allow relatively quicker searches by using algorithms such as fast-bit counting to compute the Hamming distance between two binary codes. Modern data-intensive technologies as well as increased computational and data storage resources have contributed heavily to the development of Big Data science . Bengio et al. Previous strategies and solutions for information storage and retrieval are challenged by the massive volumes of data and different data representations, both associated with Big Data. More specifically, it aids in automatically extracting complex data representations from large volumes of unsupervised data. In: Proceedings of the 25th International Conference on Software Engineering and Knowledge Engineering, Boston, MA. Socher et al.  consider the problem of training a Deep Learning neural network with billions of parameters using tens of thousands of CPU cores, in the context of speech recognition and computer vision. Miklov et al. A key task associated with Big Data Analytics is information retrieval . In addition to the obvious great volumes of data, Big Data is also associated with other specific complexities, often referred to as the four Vs: Volume, Variety, Velocity, and Veracity ,,. Lian Duan College of Computing Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA Correspondence email@example.com & Ye Xiong College of Computing … In the context of Big Data Analytics, here Deep Learning would aid in the discriminative task of semantic tagging of data. The International Journal of Data Analytics (IJDA) publishes the latest and high-quality research papers and methodologies in data analytics. The compact representations are efficient because they require fewer computations when used in indexing, and in addition, also need less storage capacity. Analytics magazine from INFORMS.  propose a Deep Learning model (based on neural networks) for domain adaptation which strives to learn a useful (for prediction purposes) representation of the unsupervised data by taking into consideration information available from the distribution shift between the training and test data. We conclude by identifying important future areas needing innovation in Deep Learning for Big Data Analytics, including data sampling for generating useful high-level abstractions, domain (data distribution) adaption, defining criteria for extracting good data representations for discriminative and indexing tasks, semi-supervised learning, and active learning. Our focus is that by presenting these works in Deep Learning, experts can observe the novel applicability of Deep Learning techniques in Big Data Analytics, particularly since some of the application domains in the works presented involve large scale data. While the possibility of data loss exists with streaming data if it is generally not immediately processed and analyzed, there is the option to save fast-moving data into bulk storage for batch processing at a later time. IEEE Vol. In practice, it is often observed that the occurrence of words are highly correlated. Hinton GE, Salakhutdinov RR (Science) Reducing the dimensionality of data with neural networks313(5786): 504–507. http://www.slideshare.net/EUDataForum/edf2013-big-datatutorialmarkogrobelnik?related=1 Grobelnik M (2013) Big Data Tutorial. For example by providing some face images to the Deep Learning algorithm, at the first layer it can learn the edges in different orientations; in the second layer it composes these edges to learn more complex features like different parts of a face such as lips, noses and eyes. pp 801–808, Le Q, Ranzato M, Monga R, Devin M, Chen K, Corrado G, Dean J, Ng A (2012) Building high-level features using large scale unsupervised learning. More specifically, they develop their own system (using neural networks) based on Commodity Off-The-Shelf High Performance Computing (COTS HPC) technology and introduce a high-speed communication infrastructure to coordinate distributed computations. pp 792–799, Mikolov T, Chen K, Dean J (2013) Efficient estimation of word representations in vector space. Determining the optimal number of model parameters in such large-scale models and improving their computational practicality pose challenges in Deep Learning for Big Data Analytics. In: International Conference on, Artificial Intelligence and Statistics. Deep learning applications and challenges in big data analytics. At this point the deep network is trained. pp 921–928, Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov R (2012) Improving neural networks by preventing co-adaptation of feature detectors. The New York Times. Big data come from many applications such as social media, sensors, Internet of Things, scientific applications, surveillance, video and image archives. IEEE. http://dx.doi.org/10.1007/978–3-642–39593–2_1 http://dx.doi.org/10.1007/978-3-642-39593-2_1 10.1007/978-3-642-39593-2_1. Stacking up the nonlinear transformation layers is the basic idea in deep learning algorithms. A targeted survey of important literature in Deep Learning research and application to different domains is presented in the paper as a means to identify how Deep Learning can be used for different purposes in Big Data Analytics. http://www.nytimes.com/2001/07/12/technology/news-watch-a-quick-way-to-search-for-images-on-the-web.html, Cusumano MA: Google: What it is and what it is not. Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The proliferation of digital … Wang W, Lu D, Zhou X, Zhang B, Mu J: Statistical wavelet-based anomaly detection in big data with compressive sensing. It is important to note that the transformations in the layers of deep architecture are non-linear transformations which try to extract underlying explanatory factors in the data. These transformations tend to disentangle factors of variations in data. Greedy layer-wise training of deep networks, Vol. The achieved final representation is a highly non-linear function of the input data. Technology based companies such as Google, Yahoo, Microsoft, and Amazon have collected and maintained data that is measured in exabyte proportions or larger. Their approach marginalizes noise in SDA training and thus does not require stochastic gradient descent or other optimization algorithms to learn parameters. 10.1162/neco.2006.18.7.1527, MATH The Le et al. The key problem in the analysis of big data is the lack of coordination between database systems as well as with analysis tools such as data mining and statistical analysis. A high-dimensional data source contributes heavily to the volume of the raw data, in addition to complicating learning from the data.
2020 big data mining and analytics journal