In cloud computing, security is an important issue due to the increasing scale of users. Current approaches to access control on clouds do not scale well to multi-tenancy requirements because they are mostly based on individual user IDs at different granularity levels. However, the number of users can be enormous and causes significant overhead in managing security. RBAC (Role-Based Access Control) is attractive because the number of roles is significantly less, and users can be classified according to their roles. This paper proposes a RBAC model using a role ontology for Multi-Tenancy Architecture (MTA) in clouds. The ontology is used to build up the role hierarchy for a specific domain. Ontology transformation operations algorithms are provided to compare the similarity of different ontology. The proposed framework can ease the design of security system in cloud and reduce the complexity of system design and implementation.

Problem resolution is a key issue in the IT service industry. A large service provider handles, on daily basis, thousands of tickets that report various types of problems from its customers. The efficiency of this process highly depends on the effective interactions among various expert groups, in search of the resolver to the reported problem. In fact, ticket transfer decisions reflect the expertise awareness between groups, thus encoding a sophisticated resolution social network. In this paper, we propose a computational framework to quantitatively assess expertise awareness, i.e., how well a group knows the expertise of others. An accurate assessment of expertise awareness could identify the weakest components in a resolution system. The framework, built on our previously developed resolution engine, is able to calculate the performance difference caused by excluding a node from the network. The difference exposes the awareness of this node to other nodes in the network. To our best knowledge, this is the first study on this problem from a computational perspective. We tested the proposed framework on a large set of real-world problem tickets and validated our discovery by carefully analyzing the tickets that are incorrectly transferred. Experimental results show that our framework can successfully capture groups that do not know others' expertise very well.

Software-as-as-Service (SaaS) is a new approach for developing software, and it is characterized by its multi-tenancy architecture and its ability to provide flexible customization to individual tenant. However, the multi-tenancy architecture and customization requirements have brought up new issues in software, such as database design, database partition, scalability, recovery, and continuous testing. This paper proposes a hybrid test database design to support SaaS customization with two-layer database partitioning. The database is further extended with a new built-in redundancy with ontology so that the SaaS can recover from ontology, data or meta-data failures. Furthermore, constraints in metadata can be used either as test cases or policies to support SaaS continuous testing and policy enforcement.

Cloud computing has received significant attention recently. This paper presents real-time issues related to cloud computing, such as multi-tenancy architecture, scheduling, paralleled computing and proposes a framework for real-time service-oriented cloud computing. Specially, we propose a novel real time architecture which solve the new challenges in Cloud Computing.

Workflows are prevalent in diverse applications, which can be scientific experiments, business processes, web services, or recipes. With the dramatically growing number of workflows, there is an increasing need for people to search a workflow repository using keywords and to retrieve the relevant ones. A workflow hierarchy is a three dimensional object containing multiple abstraction views of different granularity on the same workflow. This unique structure poses a new set of challenges compared to keyword search on tree or graph structures typically found in relational or XML data. In this paper, we define an informative, self-contained and concise search result on workflows to be a projection of a workflow hierarchy on a two dimensional viewing plane inferred from user queries. We then design and develop an efficient keyword search engine for workflows. Experimental evaluation demonstrates the effectiveness of our approach.

In this paper, we analyze the performance of an end-to-end Mortgage Origination (MO) process. The process begins with the submission of a mortgage application by an applicant to a lender and ends with one of the following outcomes: closing, i.e., loan approved by the lender and accepted by the applicant or non-closing, i.e., loan either rejected by the lender, or approved by the lender and not accepted by the applicant. Ranking mortgage applications by their predicted likelihood of closing at various steps in the process is useful for process efficiency and identification of actionable insights to convert applications likely to non-close into those that are likely to close. To build models for ranking applications at any step of the MO process, we take into account customer and product specific attributes of the applications as well as environment attributes and the history of the applications or workflow.The large state-space of the workflow makes the ranking problem challenging. We propose two workflow attributes, each with a state-space of dimension one, based on the number of visits to any step and a particular step (re-work) respectively. We find that incorporating these workflow attributes into the density modeling technique that we develop results in improvement of 4:8 percent in Average Precision over models that only incorporate customer, product and environment attributes. The simple and scalable density modeling technique allows for easy identification of applications that are likely to non-close and consequent corrective action such as change in the attributes of the mortgage product being offered. Further, our results indicate that the model is comparable to Support Vector Machines and superior to Logistic Regression for ranking.

Workflows are widely used for representing business processes, Web services, scientific experiments, and activities in daily life, like recipes. There is an increasing need for people to search a workflow repository using keywords and retrieve the relevant ones according to their interests. A workflow hierarchy is a three dimensional object containing multi-resolution abstraction views on the same workflow. This unique structure poses a new set of challenges compared to keyword search on tree or graph structures which are typically found in XML or relational data. In this demonstration, we present an effective workflow search engine, WISE, which returns informative and concise search results, defined as the minimal views of the most specific workflow hierarchies containing matching keywords.

IT problem management calls for quick identification of resolvers to reported problems. The efficiency of this process highly depends on ticket routing---transferring problem ticket among various expert groups in search of the right resolver to the ticket. To achieve efficient ticket routing, wise decision needs to be made at each step of ticket transfer to determine which expert group is likely to be, or to lead to the resolver.

Managing problem tickets is a key issue in IT service industry. A large service provider may handle thousands of problem tickets from its customers on a daily basis. The efficiency of processing these tickets highly depends on ticket routing---transferring problem tickets among expert groups in search of the right resolver to the ticket. Despite that many ticket management systems are available, ticket routing in these systems is still manually operated by support personnel. In this demo, we introduce EasyTicket, a ticket routing recommendation engine that helps automate this process. By mining ticket history data, we model an enterprise social network that represents the functional relationships among various expert groups in ticket routing. Based on this network, our system then provides routing recommendations to new tickets. Our experimental studies on 1.4 million real-world problem tickets show that on average, EasyTicket can improve the efficiency of ticket routing by 35%.

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