Email Analysis and Information Extraction for Enterprise Benefit

In spite of rapid advances in multimedia and interactive technologies, enterprise users prefer to battle with email spam and overload rather than lose the benefits of communicating, collaborating and solving business tasks over email. Many aspects of email have significantly improved over time, but its overall integration with the enterprise environment remained practically the same. In this paper we describe and evaluate a light-weight approach to enterprise email communication analysis and information extraction. We provide several use cases exploiting the extracted information, such as the enrichment of emails with relevant contextual information, social network extraction and its subsequent search, creation of semantic objects as well as the relationship between email analysis and information extraction on one hand, and email protocols and email servers on the other. The proposed approach was partially tested on several small and medium enterprises (SMEs) and seems to be promising for enterprise interoperability and collaboration in SMEs that depend on emails to accomplish their daily business tasks

Sistemas de Inteligencia Web basados en redes sociales

El Análisis de las Redes Sociales (ARS) es un área que está emergiendo como imprescindible en los procesos de toma de decisiones. Su capacidad para analizar e intervenir una red social puede ser aprovechada para implantar tareas de vigilancia en los sistemas de inteligencia de un centro de investigación o una empresa de base tecnológica. El objetivo de este trabajo es realizar una propuesta para diseñar sistemas de inteligencia web basados en redes sociales. El primer obstáculo para implantar un sistema de estas características es el proceso de recolección de datos. Con objeto de resolver este problema se presenta una metodología para extraer redes sociales. El proceso de extracción se realiza analizando los resultados ofrecidos por los motores de búsqueda. Las consultas realizadas a los motores son construidas en base a direcciones de correo electrónico. A través de la red de extraída también se analiza su distribución espacial, el impacto global de una temática y las relaciones institucionales subyacentes. Como ejemplo concreto se analiza la estructura social de la comunidad que forma la lista de distribución REDES.Social Network Analysis (SNA) is an emerging area, essential in decision making processes. Its capacities to analyze and intervene in a social network can be used to implant surveillance tasks in research centers or technological-based businesses. The aim of this work is to make a proposal to design intelligence web systems based on social networks. The first obstacle to implant these systems is the data gather process. In order to solve this problem, an extracting social networks methodology is presented. The extraction process is carried out by analyzing the search engine results. Queries are based on electronic mails. From the extracted network, its spatial distribution of social relationships, the global thematic impact and the institutional relationships are also analyzed. The social structure of REDES email distribution list is analyzed as an example

Data-driven Job Search Engine Using Skills and Company Attribute Filters

According to a report online, more than 200 million unique users search for jobs online every month. This incredibly large and fast growing demand has enticed software giants such as Google and Facebook to enter this space, which was previously dominated by companies such as LinkedIn, Indeed and CareerBuilder. Recently, Google released their "AI-powered Jobs Search Engine", "Google For Jobs" while Facebook released "Facebook Jobs" within their platform. These current job search engines and platforms allow users to search for jobs based on general narrow filters such as job title, date posted, experience level, company and salary. However, they have severely limited filters relating to skill sets such as C++, Python, and Java and company related attributes such as employee size, revenue, technographics and micro-industries. These specialized filters can help applicants and companies connect at a very personalized, relevant and deeper level. In this paper we present a framework that provides an end-to-end "Data-driven Jobs Search Engine". In addition, users can also receive potential contacts of recruiters and senior positions for connection and networking opportunities. The high level implementation of the framework is described as follows: 1) Collect job postings data in the United States, 2) Extract meaningful tokens from the postings data using ETL pipelines, 3) Normalize the data set to link company names to their specific company websites, 4) Extract and ranking the skill sets, 5) Link the company names and websites to their respective company level attributes with the EVERSTRING Company API, 6) Run user-specific search queries on the database to identify relevant job postings and 7) Rank the job search results. This framework offers a highly customizable and highly targeted search experience for end users.Comment: 8 pages, 10 figures, ICDM 201