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At DBKDA 2019, we demonstrated that StrongDBMS with simple but rigorous optimistic algorithms, provides better performance in situations of high concurrency than major commercial database management systems (DBMS). The demonstration was convincing but the reasons for its success were not fully analysed. There is a brief account of the results below. In this short contribution, we wish to discuss the reasons for the results. The analysis leads to a strong criticism of all DBMS algorithms based on locking, and based on these results, it is not fanciful to suggest that it is time to re-engineer existing DBMS.
Recent work on database application development platforms has sought to include a declarative formulation of a conceptual data model in the application code, using annotations or attributes. Some recent work has used metadata to include the details of such formulations in the physical database, and this approach brings significant advantages in that the model can be enforced across a range of applications for a single database. In previous work, we have discussed the advantages for enterprise integration of typed graph data models (TGM), which can play a similar role in graphical databases, leveraging the existing support for the unified modelling language UML. Ideally, the integration of systems designed with different models, for example, graphical and relational database, should also be supported. In this work, we implement this approach, using metadata in a relational database management system (DBMS).
New storage technologies, such as Flash and Non- Volatile Memories, with fundamentally different properties are appearing. Leveraging their performance and endurance requires a redesign of existing architecture and algorithms in modern high performance databases. Multi-Version Concurrency Control (MVCC) approaches in database systems, maintain multiple timestamped versions of a tuple. Once a transaction reads a tuple the database system tracks and returns the respective version eliminating lock-requests. Hence under MVCC reads are never blocked, which leverages well the excellent read performance (high throughput, low latency) of new storage technologies. Upon tuple updates, however, established implementations of MVCC approaches (such as Snapshot Isolation) lead to multiple random writes – caused by (i) creation of the new and (ii) in-place invalidation of the old version – thus generating suboptimal access patterns for the new storage media. The combination of an append based storage manager operating with tuple granularity and snapshot isolation addresses asymmetry and in-place updates. In this paper, we highlight novel aspects of log-based storage, in multi-version database systems on new storage media. We claim that multi-versioning and append-based storage can be used to effectively address asymmetry and endurance. We identify multi-versioning as the approach to address dataplacement in complex memory hierarchies. We focus on: version handling, (physical) version placement, compression and collocation of tuple versions on Flash storage and in complex memory hierarchies. We identify possible read- and cacherelated optimizations.
The typed graph model
(2020)
In recent years, the Graph Model has become increasingly popular, especially in the application domain of social networks. The model has been semantically augmented with properties and labels attached to the graph elements. It is difficult to ensure data quality for the properties and the data structure because the model does not need a schema. In this paper, we propose a schema bound Typed Graph Model with properties and labels. These enhancements improve not only data quality but also the quality of graph analysis. The power of this model is provided by using hyper-nodes and hyper edges, which allows to present a data structure on different abstraction levels. We demonstrate by example the superiority of this model over the property graph data model of Hidders and other prevalent data models, namely the relational, object-oriented, and XML model.
Schema and data integration have been a challenge for more than 40 years. While data warehouse technologies are quite a success story, there is still a lack of information integration methods, especially if the data sources are based on different data models or do not have a schema. Enterprise Information Integration has to deal with heterogeneous data sources and requires up-to-date high-quality information to provide a reliable basis for analysis and decision-making. The paper proposes virtual integration using the Typed Graph Model to support schema mediation. The integration process first converts the structure of each source into a typed graph schema, which is then matched to the mediated schema. Mapping rules define transformations between the schemata to reconcile semantics. The mapping can be visually validated by experts. It provides indicators and rules to achieve a consistent schema mapping, which leads to high data integrity and quality.
This paper presents a concurrency control mechanism that does not follow a ‘one concurrency control mechanism fits all needs’ strategy. With the presented mechanism a transaction runs under several concurrency control mechanisms and the appropriate one is chosen based on the accessed data. For this purpose, the data is divided into four classes based on its access type and usage (semantics). Class O (the optimistic class) implements a first-committer-wins strategy, class R (the reconciliation class) implements a first-n-committers-win strategy, class P (the pessimistic class) implements a first reader-wins strategy, and class E (the escrow class) implements a firsnreaderswin strategy. Accordingly, the model is called OjRjPjE. Under this model the TPC-C benchmark outperforms other CC mechanisms like optimistic Snapshot Isolation.
This work presents a disconnected transaction model able to cope with the increased complexity of longliving, hierarchically structured, and disconnected transactions. Wecombine an Open and Closed Nested Transaction Model with Optimistic Concurrency Control and interrelate flat transactions with the aforementioned complex nature. Despite temporary inconsistencies during a transaction’s execution our model ensures consistency.
When forecasting sales figures, not only the sales history but also the future price of a product will influence the sales quantity. At first sight, multivariate time series seem to be the appropriate model for this task. Nontheless, in real life history is not always repeatable, i.e. in the case of sales history there is only one price for a product at a given time. This complicates the design of a multivariate time series. However, for some seasonal or perishable products the price is rather a function of the expiration date than of the sales history. This additional information can help to design a more accurate and causal time series model. The proposed solution uses an univariate time series model but takes the price of a product as a parameter that influences systematically the prediction. The price influence is computed based on historical sales data using correlation analysis and adjustable price ranges to identify products with comparable history. Compared to other techniques this novel approach is easy to compute and allows to preset the price parameter for predictions and simulations. Tests with data from the Data Mining Cup 2012 demonstrate better results than established sophisticated time series methods.
The recent years and especially the Internet have changed the way on how data is stored. We now often store data together with its creation time-stamp. These data sequences potentially enable us to track the change of data over time. This is quite interesting, especially in the e-commerce area, in which classification of a sequence of customer actions, is still a challenging task for data miners. However, before Standard algorithms such as Decision Trees, Neuronal Nets, Naive Bayes or Bayesian Belief Networks can be applied on sequential data, preparations need to be done in order to capture the information stored within the sequences. Therefore, this work presents a systematic approach on how to reveal sequence patterns among data and how to construct powerful features out of the primitive sequence attributes. This is achieved by sequence aggregation and the incorporation of time dimension into the Feature construction step. The proposed algorithm is described in detail and applied on a real life data set, which demonstrates the ability of the proposed algorithm to boost the classification performance of well known data mining algorithms for classification tasks.
Entrepreneurs and small and medium enterprises usually have issues on developing new prototypes, new ideas or testing new techniques. In order to help them, in the last years, academic Software Factories, a new concept of collaboration between universities and companies have been developed. Software Factories provide a unique environment for students and companies. Students benefit from the possibility of working in a real work environment learning how to apply the state of the art of the existing techniques and showing their skills to entrepreneurs. Companies benefit from the risk-free environment where they can develop new ideas, in a protected environment. Universities finally benefit from this setup as a perfect environment for empirical studies in industrial-like environment. In this paper, we present the network of academic Software Factories in Europe, showing how companies had already benefit from existing Software Factories and reporting success stories. The results of this paper can increase the network of the factories and help other universities and companies to setup similar environment to boost the local economy.