Karuna Pande Joshi

Warehousing and mining Web logs

Analyzing Web Logs for usage and access trends can not only provide important information to web site developers and administrators, but also help in creating adaptive web sites. While there are many existing tools that generate fixed reports from web logs, they typically do not allow ad-hoc analysis queries. Moreover, such tools cannot discover hidden patterns of access embedded in the access logs. We describe a relational OLAP (ROLAP) approach for creating a web-log warehouse. This is populated both from web logs, as well as the results of mining web logs. We also present a web based ad-hoc tool for analytic queries on the warehouse. We discuss the design criteria that influenced our choice of dimensions, facts and data granularity, and present the results from analyzing and mining the logs.

On Using a Warehouse to Analyze Web Logs

Analyzing Web Logs for usage and access trends can not only provide important information to web site developers and administrators, but also help in creating adaptive web sites. While there are many existing tools that generate fixed reports from web logs, they typically do not allow ad-hoc analysis queries. Moreover, such tools cannot discover hidden patterns of access embedded in the access logs. We describe a relational OLAP (ROLAP) approach for creating a web-log warehouse. This is populated both from web logs, as well as the results of mining web logs. We discuss the design criteria that influenced our choice of dimensions, facts and data granularity. A web based ad-hoc tool for analytic queries on the warehouse was developed. We present some of the performance specific experiments that we performed on our warehouse.

Automating Cloud Services Life Cycle through Semantic Technologies

Managing virtualized services efficiently over the cloud is an open challenge. Traditional models of software development are not appropriate for the cloud computing domain, where software (and other) services are acquired on demand. In this paper, we describe a new integrated methodology for the life cycle of IT services delivered on the cloud and demonstrate how it can be used to represent and reason about services and service requirements and so automate service acquisition and consumption from the cloud. We have divided the IT service life cycle into five phases of requirements, discovery, negotiation, composition, and consumption. We detail each phase and describe the ontologies that we have developed to represent the concepts and relationships for each phase. To show how this life cycle can automate the usage of cloud services, we describe a cloud storage prototype that we have developed. This methodology complements previous work on ontologies for service descriptions in that it is focused on supporting negotiation for the particulars of a service and going beyond simple matchmaking.

Managing the Quality of Virtualized Services

Managing the quality of virtualized services that are delivered on the cloud is very challenging. Such services are often composed of smaller components that are assembled on an as-needed basis. In this paper, we propose a framework to measure and semi-automatically track quality delivered by a Virtualized service delivery system. The framework provides a mechanism to relate hard metrics typically measured at the backstage of the delivery process to quality related hard and soft metrics tracked at the front stage where the consumer interacts with the service. This allows administrators responsible for the functioning of a service to monitor its quality based on the measurements typically already done for the component services. The framework is general enough to be applied to any type of IT service. In the paper, we primarily concentrate on the Helpdesk service. We include the performance rules we have created by mining Helpdesk data.

Automatic Extraction of Metrics from SLAs for Cloud Service Management

To effectively manage cloud based services, organizations need to continuously monitor the performance metrics listed in the Cloud service contracts. However, these legal documents, like Service Level Agreements (SLA) or privacy policy documents, are currently managed as plain text files meant principally for human consumption. Additionally, providers often define their own performance metrics for their services. These factors hinder the automation of SLA management and require manual effort to monitor the cloud service performance. We have significantly automated the process of extracting, managing and monitoring cloud SLA using natural language processing techniques and Semantic Web technologies. In this paper, we describe our technical approach and the ontology that we have developed to describe, manage, and reason about cloud SLAs. We also describe the prototype system that we have developed to automatically extract information from legal Terms of Service that are available on cloud provider websites.

Automating Cloud Service Level Agreements Using Semantic Technologies

Cloud related legal documents, like terms of service or customer agreement are usually managed as plain text files. Hence extensive manual effort is required to monitor the cloud service performance by cross referencing the metrics and measures agreed upon in these documents. We have significantly automated the process of managing and monitoring cloud Service Level Agreements (SLA) using semantic web technologies like OWL, RDF and SPARQL. In this paper, we describe in detail the cloud SLA ontology and the prototype that we have developed to illustrate how the SLA measures can be automatically extracted from legal Terms of Service that are available on cloud provider websites.

Parallelizing natural language techniques for knowledge extraction from cloud service level agreements

To efficiently utilize their cloud based services, consumers have to continuously monitor and manage the Service Level Agreements (SLA) that define the service performance measures. Currently this is still a time and labor intensive process since the SLAs are primarily stored as text documents. We have significantly automated the process of extracting, managing and monitoring cloud SLAs using natural language processing techniques and Semantic Web technologies. In this paper we describe our prototype system that uses a Hadoop cluster to extract knowledge from unstructured legal text documents. For this prototype we have considered publicly available SLA/terms of service documents of various cloud providers. We use established natural language processing techniques in parallel to speed up cloud legal knowledge base creation. Our system considerably speeds up knowledge base creation and can also be used in other domains that have unstructured data.

Determining Value Co-creation Opportunity in B2B Services

In Business to Business (B2B) IT outsourcing contracts service providers have to demonstrate value continuously to retain client loyalty. However, sustained value can only be co-created by both service providers and clients though a collaborative process. This process of jointly creating value is termed value co-creation. In this paper we present a new technique that we have devised to identify the opportunities for value co-creation based on priorities for value drivers in an IT enabled B2B services contract. We illustrate this technique by applying it to the IT Helpdesk service.

Streamlining Management of Multiple Cloud Services

With the increase in the number of cloud services and service providers, manual analysis of Service Level Agreements (SLA), comparison between different service offerings and conformance regulation has become a difficult task for customers. Cloud SLAs are policy documents describing the legal agreement between cloud providers and customers. SLA specifies the commitment of availability, performance of services, penalties associated with violations and procedure for customers to receive compensations in case of service disruptions. The aim of our research is to develop technology solutions for automated cloud service management using Semantic Web and Text Mining techniques. In this paper we discuss in detail the challenges in automating cloud services management and present our preliminary work in extraction of knowledge from SLAs of different cloud services. We extracted two types of information from the SLA documents which can be useful for end users. First, the relationship between the service commitment and financial credit. We represented this information by enhancing the existing Cloud service ontology proposed by us in our previous research. Second, we extracted rules in the form of obligations and permissions from SLAs using modal and deontic logic formalizations. For our analysis, we considered six publicly available SLA documents from different cloud computing service providers.

Semantic approach to automating management of big data privacy policies

Ensuring privacy of Big Data managed on the cloud is critical to ensure consumer confidence. Cloud providers publish privacy policy documents outlining the steps they take to ensure data and consumer privacy. These documents are available as large text documents that require manual effort and time to track and manage. We have developed a semantically rich ontology to describe the privacy policy documents and built a database of several policy documents as instances of this ontology. We next extracted rules from these policy documents based on deontic logic which can be used to automate management of data privacy. In this paper we describe our ontology in detail along with the results of our analysis of privacy policies of prominent cloud services.