Scheme solutions by which this disadvantage may be avoided – with using of integrator, by their processing with monostable multivibrator are known. The difficulty in using them in the concrete case is caused by their irregular movement of the conveyor belt because of the vibrations, which strongly hampers the specifying of the time constant of the delay circuitry.
Fig.2 Scheme solution of the device for counting of bottles.
The problem is solved by using of R -S trigger, to which both inputs impulses are entering from the both photoconverters (Fig.2). Each photoconverter contains emitter and receiver. When the mouth of the bottle passes between the source and the receiver of the first photoconverter PR, the light ray is discontinued repeatedly.
The obtained output impulses enter the first (for example ”S”) input of the trigger. The first impulse fixes a certain Fourth International Conference I.TECH 2006 state - in the case logical “1” at its output and the succeeding ones do not change the output state regardless their number. When the second light ray crosses the mouth of a bottle, the obtained output impulses from the second photo receiver PR enter the second (“R“) input of the trigger. The first one of them alters the output state of the trigger into logical “0” and the succeeding ones are not of importance. Thus the obtaining of only one output impulse when a bottle passes is guaranteed.
The chosen scheme solution is characterized by extremely high reliability, high stability, simplicity and lack of necessity îf adjustment at producing and in the process of exploitation.
The main problem in designing of the construction is the right choice of the distance l between both photoconverters. In order the impulses not to enter the both inputs of the R -S trigger simultaneously this distance has to be as big as possible. But its excessive augmentation would lead to errors from missing of bottles if they do not move closely one to another. From Fig.2 it can be seen that if the ray diameter is small enough following condition has to be fulfilled:
d < l < D (1) where d - maximal diameter of the mouth of the bottle; D – minimal diameter of the body of the bottle.
On the basis of the described principle the entire block scheme of the device for counting of glass bottles on the conveyor belt (Fig.3) is developed.
Fig.3. Structural scheme of the device for counting of bottles Two identical channels, each one including light source (LS and LS ), light receiver (LR and LR ), source of 1 2 1 reference voltage (SRV and SRV ), comparator (C and C ) and matching device (MD and MD ) are used.
1 2 1 2 1 The principle of operation is illustrated through the time diagram from Fig. 4. When the light receiver LR is lighted up, the voltage of the inverting input of the comparator C is higher than the reference one (Ur). The corresponding output voltage of the comparator is low. At the output of the amplifier MD a high TTL - level is obtained as the amplifier is an inverting one. When the light receiver LR is shaded by a passing bottle at the output of the comparator C a high level is obtained and at the output of MD – low level. The R -S trigger TR is established 1 in condition “logical 1”. When the light receiver LR is shaded analogous processes occur and the trigger TR is cleared. The trigger TR eliminates the influence of the winkings.
The device has a symmetrical output. This enables sharp decreasing of the disturbances, which may penetrate through the line, connecting the output of the device to the input of the Automated information system (AIS) as well as for possible errors, caused by the disconnecting of the connecting wires at their connecting to “ground” etc. For the purpose in the receiving block of AIS a circuitry “sum of modulus two” is connected.
A control block (CB) for diagnostics and control of the normal operation [Marinov, 1980] is provided as a part of the device and through which the good working order of the LS and LS ; the output signals of the comparators, 1 Software Engineering received from MD and MD ; the signals, received from the outputs of the trigger; the presence of supply voltage 1 are supervised.
Fig.4. Operation time diagram Conclusion The designed device is a composite part of the automated information system for control, reporting and documenting the quantity of produced glass bottles, which is introduced in the factory for glass processing in town of Elena. The device enables the counting of empty bottles, discolored or of different coloring, of different form and size. It also may be successfully applied for counting of full bottles regardless the content and it level. These qualities of device provide its comparatively wide application in different branches of industry.
Bibliography [Draganov, 2006] Draganov, V.D., G.P. Toshkov, D.T. hristov. System for reading and documenting of the ready production quantity, Acta Universitatis Pontica, Volume 6, Number VII, [Solid Count, 2006] www.cornerstoneautosys.com/solidcount.htm - Solid Count [Fast Counts, 2006] http://www.accusort.com/applications/counting.html - Fast Counts [Patent 0050111724, 2005] United States Patent Application: 0050111724, A1, May 26, 2005. Method and apparatus for programmable zoned array coundter [Bergmann, 1980] Bergmann H. Fotoelektrische Schalter, Radio fernsehen elektronik, 12/1980, pp. 769-770, in German [Marinov, 1980] Marinov, Ju., E. Rangelova, V. Dimitrov. Technical diagnostics of radio-electronic systems and devices, Tehnika, Sofia, 1980, in Bulgarian Authors’ Information Ventseslav Draganov – Technical University of Varna, 1, Studentska Str, Varna, 9010, å-mail: firstname.lastname@example.org Georgi Toshkov – Technical University of Varna, 1, Studentska Str, Varna, 9010, å-mail: email@example.com Dimcho Draganov – ”Technotrade”, Varna, 9000.
Daniela Toshkova – Technical University of Varna, 1, Studentska Str, Varna, 9010, å-mail: firstname.lastname@example.org Fourth International Conference I.TECH 2006 Information Systems BUILDING DATA WAREHOUSES USING NUMBERED INFORMATION SPACES Krassimir Markov Abstract: An approach for organizing the information in the data warehouses is presented in the paper.
The possibilities of the numbered information spaces for building data warehouses are discussed. An application is outlined in the paper.
Keywords: Data Warehouses, Operational Data Stores, Numbered Information Spaces ACM Classification Keywords: E.1 Data structures, E.2 Data storage representations Introduction The origin of the Data Warehouses (DW) can be traced to studies at MIT in the 1970s which were targeted at developing an optimal technical architecture [Haisten, 2003]. The initial conception of DW had been proposed by the specialists of IBM using the concept “information warehouses” and its goal was to ensure the access to data stored in no relational systems. In 1988, Barry Devlin and Paul Murphy of IBM Ireland tackled the problem of enterprise integration head-on. They used the term "business data warehouse" and defined it as: “a repository of all required business information” or “the single logical storehouse of all the information used to report on the business” [Devlin and Murphy, 1988]. At present, the conception of “data warehouse” becomes popular mainly due to activity of Bill Inmon. In 1991, he published his first book on data warehousing. W.H. Inmon’s definition is:
“Data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision making process“ [Inmon, 1991]. Let remember, the data warehouses allow long term information about an enterprise to be recorded, summarized and presented. Usually the data warehouse is a passive observer object that takes no part in business processes, and is not part of the business model. The axes of a multidimensional data warehouse are not arbitrary, but represent real aspects of the business. Axes should represent the purpose, process, resource and organization aspects. The summary hierarchies on each of these axes should parallel the fractal structures in the business model. Roll up and drill down to zoom from summary to detail information is therefore based on the structure of the business, so is meaningful to management and other users. [Marshall, 1997].
As a rule, the typical enterprise has many different systems for operative processing with very incompatible data.
In such case, the main task is to convert the existing archives of data into a source for new knowledge which will give to the users a uniform integrated and consolidated notion of the corporate data. The old systems for operative information processing have been developed without foreseeing the support of the requirements of modern business and the need of automated support of decision making. Because of this, the converting the usual systems for online transaction processing (OLTP) in the systems for decision support (resp. – DW) were very complicated task. To solve this problem, an intermediate level has been proposed – the “operational data stores”. The Operational Data Store (ODS) is a database designed to integrate data from multiple sources to facilitate operations, analysis and reporting. Because the data originates from multiple sources, the integration Information Systems often involves cleaning, redundancy resolution and business rule enforcement. An ODS is usually designed to contain low level or atomic (indivisible) data such as transactions and prices as opposed to aggregated or summarized data such as net contributions. Aggregated data is usually stored in the DW [Wikipedia, ODS].
The definition of ODS given by Bill Inmon is: “an ODS is a subject-oriented, integrated, volatile, current-valued, detailed-only collection of data in support of an organization’s need for up-to-thesecond, operational, integrated, collective information”. [Inmon, 1995] At first glance the ODS appears to be very similar to the data warehouse in structure and content. In some respects there are strong similarities between the two types of architectural constructs. But the ODS has some very different characteristics from the data warehouse. Both the ODS and the data warehouse are subjectoriented and integrated. In that regard, the two environments are identical. Both environments require that data be integrated and transformed as it passes into the ODS and/or the data warehouse. But here the similarities between the ODS and the data warehouse end. The ODS contains volatile data while the data warehouse contains non-volatile data. Data is updated in the ODS while data is not updated in the data warehouse. Another important difference between the two environments is that the ODS contains only very current data while the data warehouse contains both current data and historical data. The data in the data warehouse is not nearly as fresh as the data in the ODS. The data warehouse contains data that is no more current than the last 24 hours. The ODS contains data that may be only seconds old. Another major difference between the two architectural constructs is that the ODS contains detailed data only. The data warehouse contains both detailed and summary data. There are then some major differences between the types of data found in the two environments. One of the most important features of the ODS is the system of record. The system of record is the formal identification of the data in the legacy environment that feeds the ODS. (Pic.1) [Inmon, 1995] Pic.1. The Operational Data Store [Inmon, 1995] So, an operational data store (ODS) is a type of database often used as an interim area for a data warehouse.
Unlike a data warehouse, which contains static data, the contents of the ODS are updated through the course of business operations. An ODS is designed to quickly perform relatively simple queries on small amounts of data (such as finding the status of a customer order), rather than the complex queries on large amounts of data are typical of the data warehouse. An ODS is similar to your short term memory in that it stores only very recent information; in comparison, the data warehouse is more like long term memory in that it stores relatively permanent information.
In the early 1990s, the original ODS systems were developed as a reporting tool for administrative purposes.
They were usually updated daily and provided reports about business transactions for that day, such as sales totals or orders filled. This type of system is now referred to as a Class III ODS. With changes in technology and business needs, the Class II ODS evolved to track more complex information such as product and location codes, and to update the database more frequently (perhaps hourly) to reflect changes. Class I ODS systems Fourth International Conference I.TECH 2006 arose from the development of customer relationship management (CRM). In Class I systems, synchronous or near-synchronous updates are used to provide customers with consistently valid and organized information.
Another version, the Class IV ODS, was recently developed with an added capacity for more interaction between the data warehouse or data mart and the ODS. [Oracle ODS] The milestone for the work presented in this paper is the simple idea that we may use a special kind of organization of the information and this way to develop easy to use and compact ODS of Class I with facilities of DW with very high speed for response which enables the real-time analytical processing (RTAP). (The RTAP multithreaded processing engine needs to support extremely large volumes of data in real time. The analytics performed are composed of combinations of algorithmic, statistical and logical functions. [B-Jensen 2002]) The investigation presented in this paper is based on the fact that a specialized form of data warehouse is the corporate financial ledger. The segments of an account code serve the same purpose as the values on the axes of a data warehouse [Marshall, 1997]. In the same time, there exist a lot of account codes in a financial ledger and it is needed to operate with great complex of tables, descriptions, reports, etc. This leads to very complicated realizations which in the most cases are paid by more and more external memory for hundreds files as well as by growing quantity of processing operations.
In other hand, well-known considerable information complexes are offered by “SAP” (Germany), “Oracle”, “PeopleSoft” (USA), etc., but the prices of such software are very high. This is serious problem for the middle and small enterprises, especially in Bulgaria, which will bankrupt if decide to implement so rich automated systems.