Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining Practical Machine Learning Tools and Techniques

A super-peer is a node in a peer-to-peer network that operates both as a server to a set of clients, and as an equal in a network of super-peers. Super-peer networks strike a balance between the efficiency of centralized search, and the autonomy, load balancing and robustness to attacks provided by distributed search. Furthermore, they take advantage of the heterogeneity of capabilities (e.g., bandwidth, processing power) across peers, which recent studies have shown to be enormous. Hence, new and old P2P systems like KaZaA and Gnutella are adopting super-peers in their design. Despite their growing popularity, the behavior of super-peer networks is not well understood. For example, what are the potential drawbacks of super-peer networks? How can super-peers be made more reliable? How many clients should a super-peer take on to maximize efficiency? we examine super-peer networks in detail, gaming an understanding of their fundamental characteristics and performance tradeoffs. We also present practical guidelines and a general procedure for the design of an efficient super-peer network.

Designing an Super-Peer Network

Introduction to Mathematical Statistics. By R.V. Hogg and A. T. Craig. Pp. ix, 245. 47s. 1959. (The Macmillan Company, New York)

Internet of Things (IoT) related applications have emerged as an important field for both engineers and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations especially in cloud computing. This paper first provides a functional framework that identifies the acquisition, management, processing and mining areas of IoT big data, and several associated technical modules are defined and described in terms of their key characteristics and capabilities. Then current research in IoT application is analyzed, moreover, the challenges and opportunities associated with IoT big data research are identified. We also report a study of critical IoT application publications and research topics based on related academic and industry publications. Finally, some open issues and some typical examples are given under the proposed IoT-related research framework.

IoT-Based Big Data Storage Systems in Cloud Computing: Perspectives and Challenges

Given a spatial location and a set of keywords, a top-k spatial keyword query returns the k best spatio-textual objects ranked according to their proximity to the query location and relevance to the query keywords. There are many applications handling huge amounts of geotagged data, such as Twitter and Flickr, that can benefit from this query. Unfortunately, the state-of-the-art approaches require non-negligible processing cost that incurs in long response time. In this paper, we propose a novel index to improve the performance of top-k spatial keyword queries named Spatial Inverted Index (S2I). Our index maps each distinct term to a set of objects containing the term. The objects are stored differently according to the document frequency of the term and can be retrieved efficiently in decreasing order of keyword relevance and spatial proximity. Moreover, we present algorithms that exploit S2I to process top-k spatial keyword queries efficiently. Finally, we show through extensive experiments that our approach outperforms the state-of-the-art approaches in terms of update and query cost.

/pdf/efficient-processing-of-top-k-spatial-keyword-queries-481i3f7p4f.pdf

Efficient processing of top-k spatial keyword queries

Enterprises today acquire vast volumes of data from different sources and leverage this information by means of data analysis to support effective decision-making and provide new functionality and services. The key requirement of data analytics is scalability, simply due to the immense volume of data that need to be extracted, processed, and analyzed in a timely fashion. Arguably the most popular framework for contemporary large-scale data analytics is MapReduce, mainly due to its salient features that include scalability, fault-tolerance, ease of programming, and flexibility. However, despite its merits, MapReduce has evident performance limitations in miscellaneous analytical tasks, and this has given rise to a significant body of research that aim at improving its efficiency, while maintaining its desirable properties. This survey aims to review the state of the art in improving the performance of parallel query processing using MapReduce. A set of the most significant weaknesses and limitations of MapReduce is discussed at a high level, along with solving techniques. A taxonomy is presented for categorizing existing research on MapReduce improvements according to the specific problem they target. Based on the proposed taxonomy, a classification of existing research is provided focusing on the optimization objective. Concluding, we outline interesting directions for future parallel data processing systems.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Doulkeridis%20-%20large-scale%20analytical%20query%20processing%20in%20MapReduce%20-%202014.pdf

A survey of large-scale analytical query processing in MapReduce

Rank-aware query processing has become essential for many applications that return to the user only the top-k objects based on the individual user's preferences. Top-k queries have been mainly studied from the perspective of the user, focusing primarily on efficient query processing. In this work, for the first time, we study top-k queries from the perspective of the product manufacturer. Given a potential product, which are the user preferences for which this product is in the top-k query result set? We identify a novel query type, namely reverse top-k query, that is essential for manufacturers to assess the potential market and impact of their products based on the competition. We formally define reverse top-k queries and introduce two versions of the query, namely monochromatic and bichromatic. We first provide a geometric interpretation of the monochromatic reverse top-k query in the solution space that helps to understand the reverse top-k query conceptually. Then, we study in more details the case of bichromatic reverse top-k query, which is more interesting for practical applications. Such a query, if computed in a straightforward manner, requires evaluating a top-k query for each user preference in the database, which is prohibitively expensive even for moderate datasets. In this paper, we present an efficient threshold-based algorithm that eliminates candidate user preferences, without processing the respective top-k queries. Furthermore, we introduce an indexing structure based on materialized reverse top-k views in order to speed up the computation of reverse top-k queries. Materialized reverse top-k views trade preprocessing cost for query speed up in a controllable manner. Our experimental evaluation demonstrates the efficiency of our techniques, which reduce the required number of top-k computations by 1 to 3 orders of magnitude.

/pdf/reverse-top-k-queries-59c8b54chw.pdf

Reverse top-k queries

With the popularization of GPS-enabled devices there is an increasing interest for location-based queries. In this context, one interesting problem is processing top-k spatial keyword queries. Given a set of objects with a textual description (e.g., menu of a restaurant), a query location (latitude and longitude), and a set of query keywords, a top-k spatial keyword query returns the k best objects ranked in terms of both distance to the query location and textual relevance to the query keywords. So far, the research on this problem has assumed Euclidean space. In order to process such queries efficiently, spatio-textual indexes combining R-trees and inverted files are employed. However, for most real applications, the distance between the objects and query location is constrained by a road network (shortest path) and cannot be computed efficiently using R-trees. In this paper, we address, for the first time, the challenging problem of processing top-k spatial keyword queries on road networks where the distance between the query location and the spatial object is the shortest path. We formalize the new query type, and present novel indexing structures and algorithms that are able to process such queries efficiently. Finally, we perform an experimental evaluation that shows the efficiency of our approach.

/pdf/top-k-spatial-keyword-queries-on-road-networks-2mgt75wb2w.pdf

Top-k spatial keyword queries on road networks

Recommendation systems have received significant attention, with most of the proposed methods focusing on personal recommendations. However, there are contexts in which the items to be suggested are not intended for a single user but for a group of people. For example, assume a group of friends or a family that is planning to watch a movie or visit a restaurant. In this paper, we propose an extensive model for group recommendations that exploits recommendations for items that similar users to the group members liked in the past. We do not exhaustively search for similar users in the whole user base, but we pre-partition users into clusters of similar ones and use the cluster members for recommendations. We efficiently aggregate the single user recommendations into group recommendations by leveraging the power of a top-k algorithm. We evaluate our approach in a real dataset of movie ratings.

/pdf/fast-group-recommendations-by-applying-user-clustering-5g9gm6zzvb.pdf

Kjetil Nørvåg

Papers

Efficient processing of top-k spatial keyword queries

A survey of large-scale analytical query processing in MapReduce

Reverse top-k queries

Top-k spatial keyword queries on road networks

Fast group recommendations by applying user clustering