Alfredo Cuzzocrea

Analytics over large-scale multidimensional data: the big data revolution!

In this paper, we provide an overview of state-of-the-art research issues and achievements in the field of analytics over big data, and we extend the discussion to analytics over big multidimensional data as well, by highlighting open problems and actual research trends. Our analytical contribution is finally completed by several novel research directions arising in this field, which plays a leading role in next-generation Data Warehousing and OLAP research.

Big data: a research agenda

Recently, a great deal of interest for Big Data has risen, mainly driven from a widespread number of research problems strongly related to real-life applications and systems, such as representing, modeling, processing, querying and mining massive, distributed, large-scale repositories (mostly being of unstructured nature). Inspired by this main trend, in this paper we discuss three important aspects of Big Data research, namely OLAP over Big Data, Big Data Posting, and Privacy of Big Data. We also depict future research directions, hence implicitly defining a research agenda aiming at leading future challenges in this research field.

XPath lookup queries in P2P networks

We address the problem of querying XML data over a P2P network. In P2P networks, the allowed kinds of queries are usually exact-match queries over file names. We discuss the extensions needed to deal with XML data and XPath queries. A single peer can hold a whole document or a partial/complete fragment of the latter. Each XML fragment/document is identified by a distinct path expression, which is encoded in a distributed hash table. Our framework differs from content-based routing mechanisms, biased towards finding the most relevant peers holding the data. We perform fragments placement and enable fragments lookup by solely exploiting few path expressions stored on each peer. By taking advantage of quasi-zero replication of global catalogs, our system supports fast full and partial XPath querying. To this purpose, we have extended the Chord simulator and performed an experimental evaluation of our approach.

Data warehousing and OLAP over big data: current challenges and future research directions

In this paper, we highlight open problems and actual research trends in the field of Data Warehousing and OLAP over Big Data, an emerging term in Data Warehousing and OLAP research. We also derive several novel research directions arising in this field, and put emphasis on possible contributions to be achieved by future research efforts.

Privacy and Security of Big Data: Current Challenges and Future Research Perspectives

Privacy and security of Big Data is gaining momentum in the research community, also due to emerging technologies like Cloud Computing, analytics engines and social networks. In response of this novel research challenge, several privacy and security of big data models, techniques and algorithms have been proposed recently, mostly adhering to algorithmic paradigms or model-oriented paradigms. Following this major trend, in this paper we provide an overview of state-of-the-art research issues and achievements in the field of privacy and security of big data, by highlighting open problems and actual research trends, and drawing novel research directions in this field.

Balancing accuracy and privacy of OLAP aggregations on data cubes

In this paper we propose an innovative framework based on flexible sampling-based data cube compression techniques for computing privacy preserving OLAP aggregations on data cubes while allowing approximate answers to be efficiently evaluated over such aggregations. In our proposal, this scenario is accomplished by means of the so-called accuracy/privacy contract, which determines how OLAP aggregations must be accessed throughout balancing accuracy of approximate answers and privacy of sensitive ranges of multidimensional data.

Discovering Frequent Patterns from Uncertain Data Streams with Time-Fading and Landmark Models

Streams of data can be continuously generated by sensors in various real-life applications such as environment surveillance. Partially due to the inherited limitation of the sensors, data in these streams can be uncertain. To discover useful knowledge in the form of frequent patterns from streams of uncertain data, a few algorithms have been developed. They mostly use the sliding window model for processing and mining data streams. However, for some applications, other stream processing models such as the time-fading model and the landmark model are more appropriate. In this paper, we propose mining algorithms that use (i) the time-fading model and (ii) the landmark model to discover frequent patterns from streams of uncertain data.

Providing probabilistically-bounded approximate answers to non-holistic aggregate range queries in OLAP

A novel framework for providing probabilistically-bounded approximate answers to non-holistic aggregate range queries in OLAP is presented in this paper. Such a framework allows us to efficiently support OLAP applications, as answering queries is the main bottleneck for this kind of applications. To this end, scalability of the techniques and accuracy of the answers are recognized as important limitations of state-of-the-art approximate query answering proposals in OLAP. Specifically, this paper is focused on the latter limitation, whereas it refers to results presented in [9] for the first one. The KSyn synopsis data structure, which implements the guidelines of the proposed framework and overcomes the recognized limitations, is also presented and discussed in detail, along with a query-conscious error metrics-based storage space allocation scheme. Finally, encouraging preliminary experimental results stating the goodness of our proposal are presented and discussed.

Analytics over Big Data: Exploring the Convergence of DataWarehousing, OLAP and Data-Intensive Cloud Infrastructures

This paper explores the convergence of Data Warehousing, OLAP and data-intensive Cloud Infrastructures in the context of so-called analytics over Big Data. The paper briefly reviews some state-of-the-art proposals, highlights open research issues and, finally, it draws possible research directions in this scientific field.

Mining constrained frequent itemsets from distributed uncertain data

Nowadays, high volumes of massive data can be generated from various sources (e.g.,sensor data from environmental surveillance). Many existing distributed frequent itemset mining algorithms do not allow users to express the itemsets to be mined according to their intention via the use of constraints. Consequently, these unconstrained mining algorithms can yield numerous itemsets that are not interesting to users. Moreover, due to inherited measurement inaccuracies and/or network latencies, the data are often riddled with uncertainty. These call for both constrained mining and uncertain data mining. In this journal article, we propose a data-intensive computer system for tree-based mining of frequent itemsets that satisfy user-defined constraints from a distributed environment such as a wireless sensor network of uncertain data. We proposed a system for tree-based distributed uncertain frequent itemset mining.Our system allows users to specify constraints for expressing their interests.It finds frequent itemsets that satisfy succinct constraints from distributed uncertain data.It also handles non-succinct (e.g.,inductive succinct, anti-monotone) constraints.