There are many benefits to upgrading your data access layer to ADO.NET, most of which involve using the intrinsic DataSet object. The DataSet object is basically a disconnected, in-memory replica of a database. DataSets provide many benefits, but also present a few challenges. Specifically, you can run into problems related to data concurrency exceptions. I've created a simple Windows® Forms customer service application that illustrates the potential pitfalls of this particular problem. I'll walk you through my research and show you ways to overcome the data concurrency issues that arose. DataSets provide a number of benefits. For example, you gain the ability to enforce rules of integrity in memory rather than at the database level. The most important benefit of using DataSets, however, is improved performance. Since the DataSet is disconnected from the underlying database, your code will make fewer calls to the database, significantly boosting performance. As with most performance optimizations, this one comes with a price. Since the DataSet object is disconnected from the underlying database, there is always a chance that the data is out of date. Since a DataSet doesn't hold live data, but rather a snapshot of live data at the time the DataSet was filled, problems related to data concurrency can occur.

Pateiktas lygčių ir nelygybių simbolinio sprendimo lygiagretus algoritmas ir jo analizė, palyginimas su neefektyvia algoritmo realizacija. Atliktas įgyvendinto algoritmo tyrimas, nustatant jo spartos priklausomybes nuo aplinkos ir užduoties, palyginant rezultatus su esama PĮ. Taip pat, šiame darbe aptariami sukurtos programų sistemos architektūriniai sprendimai: MVC patern‘as (design pattern), „Svogūno“ architektūra, priklausomybių injekcijos (Dependency Injections). Šie architektūriniai sprendimai yra pranašesni už standartinę sluoksninę architekūrą, jais paremta PĮ yra lengviau palaikoma ir modifikuojama. Šiais laikais dauguma kompiuterių turi daugiabranduolius procesorius, tačiau esama PĮ jų neišnaudoja. Šio darbo tikslas yra sukurti tokią lygčių ir nelygybių simbolinio sprendimo lygiagrečiu metodu realizaciją, kuri panaudodama turimą skaičiavimų galią, sutrumpintų skaičiavimų laiką. Atlikus tyrimus nustatyta, jog sukurtoji PĮ yra pranašesnė už Maple CAS tik tuo atveju, kai uždavinio sąlyga nėra didelė, bet reikalaujama didelės skaičiavimų galios (nelygybių sistemų sprendimas). Tačiau sprendžiant didelės apimties lygčių sistemas (40-50 nežinomųjų ir tiek pat lygčių) sukurtoji PĮ atsilieka nuo Maple CAS, kadangi daug laiko sugaištama nagrinėjant pateiktą užduotį ir skaidant ją į dalinius uždavinius. / I have presented an effective way to solve symbolic systems of equations and inequalities using parallel processes and compared it to ineffective method. Also, I have performed analysis of presented algorithm, determining its performance dependencies and comparing its performance to existing software. Also, this paper discusses architectural solutions for the application system: MVC design pattern, "Onion" architecture and Dependency Injection. These architectural patterns benefit more than standard layered architecture, software, based on these patterns, is more maintainable and changeable. These days, computers usually have multi-core processors, but not all software use them efficiently. The main problem is to create algorithm for solving symbolic systems of equations and inequalities using parallel processes, using calculation power and decreasing calculation time. Such application system was created and analyzed in this paper. It was determined that created software is superior to Maple CAS when task is small by input but requires a lot of calculating power (systems of inequalities). On the other hand, results differ when task consist of plenty of equations (40-50 equations in system, same number of unknowns). Created software falls behind Maple CAS in performance. The main reason, for this, is that created software spends too much time to analyze task and strings in it.