Alfredo Alba

Towards a Formal Definition of a Computing Cloud

Cloud computing has been endorsed by the IT community as the new paradigm shift in the industry that charts the way forward. Unfortunately, the field is still on its path to rigor and robustness. This is epitomized by the numerous fuzzy articulations of “what is cloud computing”. This paper makes a first attempt at remedying this conundrum by providing a core technical specification of the model for cloud computing and demonstrating how current and future cloud deployments can use this to foster more productive technical discussion in future.

Efficient and agile storage management in software defined environments

The IT industry is experiencing a disruptive trend for which the entire data center infrastructure is becoming software defined and programmable. IT resources are provisioned and optimized continuously according to a declarative and expressive specification of the workload requirements. The software defined environments facilitate agile IT deployment and responsive data center configurations that enable rapid creation and optimization of value-added services for clients. However, this fundamental shift introduces new challenges to existing data center management solutions. In this paper, we focus on the storage aspect of the IT infrastructure and investigate its unique challenges as well as opportunities in the emerging software defined environments. Current state-of-the-art software defined storage (SDS) solutions are discussed, followed by our novel framework to advance the existing SDS solutions. In addition, we study the interactions among SDS, software defined compute (SDC), and software defined networking (SDN) to demonstrate the necessity of a holistic orchestration and to show that joint optimization can significantly improve the effectiveness and efficiency of the overall software defined environments.

Accessing the deep web: when good ideas go bad

Prevailing wisdom assumes that there are well-defined, effective and efficient methods for accessing Deep Web content. Unfortunately, there are a host of technical and non-technical factors that may call this assumption into question. In this paper, we present the findings from work on a software system, which was commissioned by the British Broadcasting Corporation (BBC). The system requires stable and periodic extraction of Deep Web content from a number of online data sources. The insight from the project brings an important issue to the forefront and under-scores the need for further research into access technology for the Deep Web.

SIMPLE: A Strategic Information Mining Platform for Licensing and Execution

Intellectual Properties (IP), such as patents and trademarks, are one of the most critical assets in today’s enterprises and research organizations. They represent the core innovation and differentiators of an organization. When leveraged effectively, they not only protect a business from its competition, but also generate significant opportunities in licensing, execution, long term research and innovation. In certain industries, e. g., Pharmaceutical industry, patents lead to multi-billion dollar revenue per year. In this paper, we present a holistic information mining solution, called SIMPLE, which mines large corpus of patents and scientific literature for insights. Unlike much prior work that deals with specific aspects of analytics, SIMPLE is an integrated and end-to-end IP analytics solution which addresses a wide range of challenges in patent analytics such as the data complexity, scale, and nomenclature issues. It encompasses techniques for patent data processing and modeling, analytics algorithms, web interface and web services for analytics service delivery and end-user interaction. We use real-world case studies to demonstrate the effectiveness of SIMPLE.

A smarter process for sensing the information space

As a result of the growth of the Internet, the amount of available information is exponentially increasing. However, increasing the amount of information does not imply increasing usefulness. Furthermore, as the complexity of business relationships increases, there is a natural tendency toward less structured interaction between entities. This highlights the growing relevance of unstructured information in documenting the interactions of organizations and individuals. Analyzing and making sense of this unstructured information space requires more than text-mining algorithms; it requires a strategic approach. We propose a unified approach that addresses a variety of information space analytics problems. Our method for making sense of unstructured data is described by six steps that are analogous to the algebraic order of operations PEMDAS (parenthesis, exponent, multiplication, division, addition, and subtraction). These basic text-mining operations can be combined in many interesting ways to handle a diverse set of problems, and just as in algebra, it is critical that these operations be performed in the correct order to guarantee a meaningful result. In this paper, we describe how PEMDAS has been implemented within organizations to enable decisions that produced measurable business value.

SIMPLE: Interactive Analytics on Patent Data

Intellectual Properties (IP), such as patents and trademarks, are one of the most critical assets in today’s enterprises and research organizations. They represent the core innovation and differentiators of an organization. When leveraged effectively, they not only protect freedom of action, but also generate significant opportunities in licensing, execution, long term research and innovation. In this paper, we expand upon a previous paper describing a solution called SIMPLE, which mines large corpus of patents and scientific literature for insights. In this paper we focus on the interactive analytics aspects of SIMPLE, which allow the analyst to explore large unstructured information collections containing mixed information in a dynamic way. We use real-world case studies to demonstrate the effectiveness of interactive analytics in SIMPLE.

MONGOOSE: MONitoring Global Online Opinions via Semantic Extraction

The ever increasing amount of content on the Internet has fostered many efforts seeking to leverage this potentially yottascale information source. Service systems using advanced data and text analytics techniques have been developed to perform knowledge gathering and information discovery over Web data. Information gathered from free and public sources on the Web is frequently integrated with enterprise and proprietary data to create sophisticated service systems able to provide insight in an increasing number of business critical areas. Unfortunately, for fixed and or limited resource projects, consistent and reliable ingestion and integration of content often dominates the effort, reducing the time available for developing core analytics and presentations that differentiate and define an information service. If this initial data extraction, translation and loading of information (known as ETL in the database world) can be abstracted for these web sources, it would provide an important core technology on which Web-based information services could be more rapidly and inexpensively developed and deployed. This paper presents such a system - MONGOOSE - an approach that seeks to reduce the time spent creating a reliable data ingest and integration system and thus reducing the time-to-impact of advanced analytics service solutions.

Mining Relations from Unstructured Content.

Extracting relations from unstructured Web content is a challenging task and for any new relation a significant effort is required to design, train and tune the extraction models. In this work, we investigate how to obtain suitable results for relation extraction with modest human efforts, relying on a dynamic active learning approach. We propose a method to reliably generate high quality training/test data for relation extraction - for any generic user-demonstrated relation, starting from a few user provided examples and extracting valuable samples from unstructured and unlabeled Web content. To this extent we propose a strategy which learns how to identify the best order to human-annotate data, maximizing learning performance early in the process. We demonstrate the viability of the approach (i) against state of the art datasets for relation extraction as well as (ii) a real case study identifying text expressing a causal relation between a drug and an adverse reaction from user generated Web content.

Sonora: A Prescriptive Model for Message Authoring on Twitter

Within social networks, certain messages propagate with more ease or attract more attention than others. This effect can be a consequence of several factors, such as topic of the message, number of followers, real-time relevance, person who is sending the message etc. Only one of these factors is within a user’s reach at authoring time: how to phrase the message. In this paper we examine how word choice contributes to the propagation of a message.

Change Detection and Correction Facilitation for Web Applications and Services

There are a large number of websites serving valuable content that can be used by higher-level applications, Web Services, Mashups, etc. Yet, due to various reasons (lack of computing resources, financial constraints etc.) they are unable to provide Web Service APIs to access their data. In their desire to incorporate the latest and greatest technologies, as well as to adapt layouts that are more preferred by users, websites undergo changes over time. These changes can range from minor, e.g. function name changes, to major, e.g., shifting the web platform to AJAX technologies. This paper addresses the problem of detecting layout changes for websites which are unable to provide any Web Service to access their content, yet do not mind others harvesting said content.