Modern stream applications necessitate the handling of large numbers of continuous queries specified over high volume data streams. This dissertation proposes novel solutions to continuous query optimization in three core areas of stream query processing, namely state-slice based multiple continuous query sharing, ring-based multi-way join query distribution and scalable distributed multi-query optimization. The first part of the dissertation proposes efficient optimization strategies that utilize the novel state-slicing concept to achieve maximum memory and computation sharing for stream join queries with window constraints. Extensive analytical and experimental evaluations demonstrate that our proposed strategies is capable to minimize the memory or CPU consumptions for multiple join queries. The second part of this dissertation proposes a novel scheme for the distributed execution of generic multi-way joins with window constraints. The proposed scheme partitions the states into disjoint slices in the time domain, and then distributes the fine-grained states in the cluster, forming a virtual computation ring. New challenges to support this distributed state-slicing processing are answered by numerous new techniques. The extensive experimental evaluations show that the proposed strategies achieve significant performance improvements in terms of response time and memory usages for a wide range of configurations and workloads on a real system. Ring based distributed stream query processing and multi-query sharing both are based on the state-slice concept. The third part of this dissertation combines the first two parts of this dissertation work and proposes a novel distributed multi-query optimization technique.

The purpose of thesis is optimization information system and its upgrade with some new functions. Optimization is based on users experience and edit using processes. Upgrade will be make new chances and functions which will help company in decision processes and it contributes to more effective work with firm’s data.

Idag är det vanligt på större IT-företag att konstellationen i de agila teamen ändras i och med projektens kompetensbehov. Förutom de egna anställda, hyrs ofta konsulter in med spetskompetens inom olika områden. För att effektivisera projektplanering, kompetensutveckling och för att hitta kompetenser i andra team på företaget, behövdes ett verktyg för att hantera kompetenserna på företaget. Denna rapport bygger på ett uppdrag förmedlat av Transportstyrelsen. Transportstyrelsens IT-enhet växer och ett stort antal konsulter är ständigt på plats. Uppdraget gick ut på att ta fram en prototyp till en webbaserad plattform för att hantera de olika teamens kompetenser. Projektet resulterade i en webbapplikation med ett användargränssnitt byggt i Angular med data lagrad i en code first-baserad SQL-server. Kommunikationen mellan Angular och SQL-servern görs över ett Web-Api. / Today, it is common for large IT companies that the constellation of the agile teams changes based on the projects skills requirements. In addition to their own employees, consultants are often hired with excellence in different areas. In order to streamline project planning, skills development and finding skills in other teams in the company, a tool was required to manage the skills in the company.   This report is based on a mission conveyed by the Swedish transport Agency. The Transport Agency's IT unit is growing and a large number of consultants are constantly present. The mission was to produce a prototype of a web-based platform to handle teams various skills.   The project resulted in a web application with a user interface built in Angular with data stored in a code first-based SQL server. Communication between Angular and the SQL Server is done over a Web Api.

This thesis is aimed to analyze the current state of information system for eshop creation based on dropshipping model and the efficiency of the developement team. Goal of this thesis is to propose and implement technical improvements based on this analysis while using chosen project management principles.

Large-scale data analysis relies on custom code both for preparing the data for analysis as well as for the core analysis algorithms. The map-reduce framework offers a simple model to parallelize custom code, but it does not integrate well with relational databases. Likewise, the literature on optimizing queries in relational databases has largely ignored user-defined functions (UDFs). In this paper, we discuss annotations for user-defined functions that facilitate optimizations that both consider relational operators and UDFs. We believe this to be the superior approach compared to just linking map-reduce evaluation to a relational database because it enables a broader range of optimizations. In this paper we focus on optimizations that enable the parallel execution of relational operators and UDFs for a number of typical patterns. A study on real-world data investigates the opportunities for parallelization of complex data flows containing both relational operators and UDFs.